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894 | util | https://github.com/ofek/pypinfo | [] | null | [] | [] | null | null | null | ofek/pypinfo | pypinfo | 386 | 38 | 14 | Python | null | Easily view PyPI download statistics via Google's BigQuery. | ofek | 2024-01-06 | 2017-05-13 | 350 | 1.101508 | null | Easily view PyPI download statistics via Google's BigQuery. | ['bigquery', 'statistics'] | ['bigquery', 'statistics'] | 2023-10-05 | [('hugovk/pypistats', 0.7068412899971008, 'util', 1), ('googleapis/python-bigquery', 0.5070151686668396, 'data', 0)] | 11 | 7 | null | 0.19 | 0 | 0 | 81 | 3 | 0 | 4 | 4 | 0 | 0 | 90 | 0 | 24 |
1,730 | util | https://github.com/grantjenks/blue | ['code-quality'] | null | [] | [] | null | null | null | grantjenks/blue | blue | 370 | 21 | 9 | Python | https://blue.readthedocs.io/ | The slightly less uncompromising Python code formatter. | grantjenks | 2024-01-12 | 2020-09-02 | 177 | 2.080321 | null | The slightly less uncompromising Python code formatter. | ['autopep8', 'black', 'code', 'codeformatter', 'formatter', 'gofmt', 'pyfmt', 'yapf'] | ['autopep8', 'black', 'code', 'code-quality', 'codeformatter', 'formatter', 'gofmt', 'pyfmt', 'yapf'] | 2022-10-27 | [('psf/black', 0.9170903563499451, 'util', 7), ('hhatto/autopep8', 0.7624608278274536, 'util', 2), ('google/yapf', 0.749129056930542, 'util', 2), ('astral-sh/ruff', 0.6735846996307373, 'util', 1), ('pycqa/flake8', 0.6205138564109802, 'util', 1), ('google/pytype', 0.6101509928703308, 'typing', 1), ('rubik/radon', 0.6026664972305298, 'util', 0), ('pygments/pygments', 0.5901092886924744, 'util', 0), ('instagram/monkeytype', 0.5733786225318909, 'typing', 1), ('landscapeio/prospector', 0.5565185546875, 'util', 0), ('nedbat/coveragepy', 0.5528746843338013, 'testing', 0), ('pypy/pypy', 0.5494239926338196, 'util', 0), ('python/mypy', 0.5406695604324341, 'typing', 1), ('pycqa/pylint-django', 0.5386630892753601, 'util', 0), ('agronholm/typeguard', 0.5374644994735718, 'typing', 1), ('willmcgugan/rich', 0.5356051325798035, 'term', 0), ('google/latexify_py', 0.5342947244644165, 'util', 0), ('hoffstadt/dearpygui', 0.5329349040985107, 'gui', 0), ('eugeneyan/python-collab-template', 0.5308860540390015, 'template', 0), ('microsoft/pyright', 0.529589056968689, 'typing', 1), ('nbqa-dev/nbqa', 0.5135296583175659, 'jupyter', 2), ('facebook/pyre-check', 0.5125738382339478, 'typing', 1), ('danielnoord/pydocstringformatter', 0.5090311169624329, 'util', 1), ('pytoolz/toolz', 0.5073704719543457, 'util', 0), ('cython/cython', 0.5051524043083191, 'util', 0), ('hadialqattan/pycln', 0.5049071311950684, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5027827024459839, 'study', 0), ('microsoft/pycodegpt', 0.5024159550666809, 'llm', 0), ('pycqa/isort', 0.5017447471618652, 'util', 2)] | 11 | 3 | null | 0 | 2 | 0 | 41 | 15 | 0 | 3 | 3 | 2 | 2 | 90 | 1 | 24 |
1,300 | diffusion | https://github.com/albarji/mixture-of-diffusers | [] | null | ['2302.02412'] | [] | null | null | null | albarji/mixture-of-diffusers | mixture-of-diffusers | 356 | 34 | 7 | Python | null | Mixture of Diffusers for scene composition and high resolution image generation | albarji | 2024-01-13 | 2022-08-23 | 75 | 4.746667 | null | Mixture of Diffusers for scene composition and high resolution image generation | ['ai', 'computer-vision', 'diffusion-models', 'stable-diffusion'] | ['ai', 'computer-vision', 'diffusion-models', 'stable-diffusion'] | 2023-05-21 | [('compvis/latent-diffusion', 0.6906744837760925, 'diffusion', 0), ('stability-ai/stablediffusion', 0.690674364566803, 'diffusion', 0), ('huggingface/diffusers', 0.6519318222999573, 'diffusion', 1), ('carson-katri/dream-textures', 0.6328878998756409, 'diffusion', 2), ('nateraw/stable-diffusion-videos', 0.5622400045394897, 'diffusion', 1), ('compvis/stable-diffusion', 0.5530049204826355, 'diffusion', 0), ('openai/glide-text2im', 0.5093878507614136, 'diffusion', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5000970959663391, 'web', 0)] | 3 | 1 | null | 0.81 | 2 | 1 | 17 | 8 | 6 | 6 | 6 | 2 | 0 | 90 | 0 | 24 |
210 | util | https://github.com/tiangolo/poetry-version-plugin | [] | null | [] | [] | null | null | null | tiangolo/poetry-version-plugin | poetry-version-plugin | 332 | 30 | 4 | Python | null | Poetry plugin for dynamically extracting the package version from a __version__ variable or a Git tag. | tiangolo | 2024-01-13 | 2021-05-27 | 139 | 2.376278 | null | Poetry plugin for dynamically extracting the package version from a __version__ variable or a Git tag. | ['packaging', 'packaging-for-pypi', 'python-poetry'] | ['packaging', 'packaging-for-pypi', 'python-poetry'] | 2023-11-04 | [('mtkennerly/poetry-dynamic-versioning', 0.6403163075447083, 'util', 0), ('python-poetry/poetry', 0.6390268206596375, 'util', 1), ('mitsuhiko/rye', 0.5633344054222107, 'util', 1), ('python-poetry/install.python-poetry.org', 0.5517739653587341, 'util', 0), ('indygreg/pyoxidizer', 0.5493191480636597, 'util', 1), ('thoth-station/micropipenv', 0.5446652173995972, 'util', 0), ('pdm-project/pdm', 0.54221111536026, 'util', 1), ('pypi/warehouse', 0.537349283695221, 'util', 0), ('tezromach/python-package-template', 0.5260007977485657, 'template', 0), ('pypa/flit', 0.519473671913147, 'util', 1), ('pypa/hatch', 0.5143115520477295, 'util', 1)] | 3 | 1 | null | 0.15 | 19 | 5 | 32 | 2 | 1 | 1 | 1 | 19 | 15 | 90 | 0.8 | 24 |
452 | gis | https://github.com/openaddresses/pyesridump | [] | null | [] | [] | null | null | null | openaddresses/pyesridump | pyesridump | 294 | 68 | 14 | Python | null | Scrapes an ESRI MapServer REST endpoint to spit out more generally-usable geodata. | openaddresses | 2024-01-11 | 2013-12-06 | 529 | 0.555166 | https://avatars.githubusercontent.com/u/6895392?v=4 | Scrapes an ESRI MapServer REST endpoint to spit out more generally-usable geodata. | [] | [] | 2023-09-26 | [('nasdaq/data-link-python', 0.5270365476608276, 'finance', 0)] | 14 | 5 | null | 0.13 | 4 | 1 | 123 | 4 | 1 | 2 | 1 | 4 | 5 | 90 | 1.2 | 24 |
465 | math | https://github.com/lukaszahradnik/pyneuralogic | [] | null | [] | [] | null | null | null | lukaszahradnik/pyneuralogic | PyNeuraLogic | 261 | 19 | 6 | Python | https://pyneuralogic.readthedocs.io/ | PyNeuraLogic lets you use Python to create Differentiable Logic Programs | lukaszahradnik | 2024-01-06 | 2020-12-06 | 164 | 1.588696 | null | PyNeuraLogic lets you use Python to create Differentiable Logic Programs | ['deep-learning', 'differentiable-programming', 'geometric-deep-learning', 'graph-neural-networks', 'logic-programming', 'machine-learning', 'pytorch', 'relational-learning'] | ['deep-learning', 'differentiable-programming', 'geometric-deep-learning', 'graph-neural-networks', 'logic-programming', 'machine-learning', 'pytorch', 'relational-learning'] | 2024-01-10 | [('pypy/pypy', 0.5711145997047424, 'util', 0), ('google/pyglove', 0.5553054809570312, 'util', 1), ('explosion/thinc', 0.5442025065422058, 'ml-dl', 3), ('gradio-app/gradio', 0.5357550978660583, 'viz', 2), ('explosion/spacy', 0.5333031415939331, 'nlp', 2), ('evhub/coconut', 0.5328680872917175, 'util', 0), ('pyro-ppl/pyro', 0.5193347930908203, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.5184630155563354, 'ml', 1), ('pytorch/rl', 0.5153552889823914, 'ml-rl', 2), ('dylanhogg/awesome-python', 0.5141112804412842, 'study', 2), ('cython/cython', 0.5075069665908813, 'util', 0), ('ml-tooling/opyrator', 0.5073249936103821, 'viz', 1), ('pytoolz/toolz', 0.5010553002357483, 'util', 0)] | 3 | 2 | null | 0.96 | 0 | 0 | 38 | 0 | 9 | 14 | 9 | 0 | 0 | 90 | 0 | 24 |
1,184 | llm | https://github.com/ai21labs/in-context-ralm | ['retrieval-augmentation', 'language-model'] | In-Context Retrieval-Augmented Language Models | [] | [] | null | null | null | ai21labs/in-context-ralm | in-context-ralm | 199 | 19 | 5 | Python | null | null | ai21labs | 2024-01-12 | 2023-01-26 | 52 | 3.775068 | https://avatars.githubusercontent.com/u/33798954?v=4 | In-Context Retrieval-Augmented Language Models | [] | ['language-model', 'retrieval-augmentation'] | 2023-12-20 | [('intellabs/fastrag', 0.6507914066314697, 'nlp', 1), ('srush/minichain', 0.5984602570533752, 'llm', 1), ('luohongyin/sail', 0.5805896520614624, 'llm', 1), ('facebookresearch/dpr-scale', 0.5703505873680115, 'nlp', 0), ('paddlepaddle/rocketqa', 0.5692198872566223, 'nlp', 0), ('freedomintelligence/llmzoo', 0.5410796403884888, 'llm', 1), ('ai21labs/lm-evaluation', 0.5293609499931335, 'llm', 1), ('muennighoff/sgpt', 0.5289058685302734, 'llm', 1), ('openlmlab/moss', 0.5220905542373657, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5203155875205994, 'llm', 1), ('ddangelov/top2vec', 0.5187664031982422, 'nlp', 0), ('ukplab/sentence-transformers', 0.5168312788009644, 'nlp', 0), ('sebischair/lbl2vec', 0.5090823173522949, 'nlp', 0), ('openlmlab/leval', 0.5012268424034119, 'llm', 1)] | 1 | 0 | null | 0.35 | 5 | 5 | 12 | 1 | 0 | 0 | 0 | 5 | 10 | 90 | 2 | 24 |
399 | study | https://github.com/dylanhogg/awesome-python | ['awesome'] | null | [] | ['<hide>'] | null | null | null | dylanhogg/awesome-python | awesome-python | 198 | 15 | 7 | HTML | https://www.awesomepython.org | 🐍 Hand-picked awesome Python libraries and frameworks, with an emphasis on data and machine learning, organised by category | dylanhogg | 2024-01-12 | 2020-06-20 | 188 | 1.050796 | null | 🐍 Hand-picked awesome Python libraries and frameworks, with an emphasis on data and machine learning, organised by category | ['awesome', 'awesome-list', 'awesome-python', 'chatgpt', 'data', 'data-science', 'deep-learning', 'jupyter', 'machine-learning', 'natural-language-processing', 'nlp', 'open-source', 'pandas'] | ['awesome', 'awesome-list', 'awesome-python', 'chatgpt', 'data', 'data-science', 'deep-learning', 'jupyter', 'machine-learning', 'natural-language-processing', 'nlp', 'open-source', 'pandas'] | 2024-01-14 | [('krzjoa/awesome-python-data-science', 0.7601116299629211, 'study', 6), ('timofurrer/awesome-asyncio', 0.7224661111831665, 'study', 2), ('gradio-app/gradio', 0.6672014594078064, 'viz', 3), ('christoschristofidis/awesome-deep-learning', 0.6466501355171204, 'study', 4), ('fastai/fastcore', 0.6330905556678772, 'util', 0), ('pandas-dev/pandas', 0.6308945417404175, 'pandas', 2), ('plotly/dash', 0.619914174079895, 'viz', 2), ('merantix-momentum/squirrel-core', 0.6165292263031006, 'ml', 5), ('paddlepaddle/paddlenlp', 0.6097233295440674, 'llm', 1), ('rasbt/mlxtend', 0.6053752899169922, 'ml', 2), ('featurelabs/featuretools', 0.6018655300140381, 'ml', 2), ('polyaxon/datatile', 0.6012967228889465, 'pandas', 2), ('pytoolz/toolz', 0.5974959135055542, 'util', 0), ('trananhkma/fucking-awesome-python', 0.5963839888572693, 'study', 1), ('ta-lib/ta-lib-python', 0.59423828125, 'finance', 0), ('holoviz/panel', 0.5920050144195557, 'viz', 1), ('pycaret/pycaret', 0.5809772610664368, 'ml', 2), ('goldmansachs/gs-quant', 0.5735217928886414, 'finance', 0), ('pypy/pypy', 0.5712080597877502, 'util', 0), ('firmai/industry-machine-learning', 0.5685208439826965, 'study', 2), ('masoniteframework/masonite', 0.5680248141288757, 'web', 0), ('eventual-inc/daft', 0.5679042935371399, 'pandas', 3), ('eleutherai/pyfra', 0.5674682259559631, 'ml', 0), ('dylanhogg/crazy-awesome-crypto', 0.5653820037841797, 'crypto', 3), ('fchollet/deep-learning-with-python-notebooks', 0.5646929144859314, 'study', 0), ('willmcgugan/textual', 0.5644351243972778, 'term', 0), ('huggingface/huggingface_hub', 0.5635517835617065, 'ml', 3), ('1200wd/bitcoinlib', 0.5614901185035706, 'crypto', 0), ('cython/cython', 0.5576968193054199, 'util', 0), ('dagworks-inc/hamilton', 0.5567782521247864, 'ml-ops', 3), ('pallets/flask', 0.5550673604011536, 'web', 0), ('tensorly/tensorly', 0.5549004673957825, 'ml-dl', 1), ('explosion/thinc', 0.5546610951423645, 'ml-dl', 4), ('klen/muffin', 0.5525697469711304, 'web', 0), ('huggingface/datasets', 0.5519663095474243, 'nlp', 5), ('jovianml/opendatasets', 0.5494063496589661, 'data', 2), ('plotly/plotly.py', 0.549170970916748, 'viz', 0), ('clips/pattern', 0.5490561723709106, 'nlp', 2), ('wesm/pydata-book', 0.548250138759613, 'study', 0), ('explosion/spacy', 0.5455989241600037, 'nlp', 5), ('man-group/dtale', 0.5450599193572998, 'viz', 2), ('probml/pyprobml', 0.5428842306137085, 'ml', 1), ('vaexio/vaex', 0.5425941944122314, 'perf', 2), ('huggingface/transformers', 0.5408051609992981, 'nlp', 4), ('evhub/coconut', 0.5391582250595093, 'util', 0), ('rasahq/rasa', 0.536875307559967, 'llm', 3), ('kubeflow-kale/kale', 0.5359322428703308, 'ml-ops', 1), ('hoffstadt/dearpygui', 0.5330666899681091, 'gui', 0), ('ibis-project/ibis', 0.5329792499542236, 'data', 1), ('ml-tooling/opyrator', 0.532485842704773, 'viz', 1), ('reloadware/reloadium', 0.5307287573814392, 'profiling', 2), ('ageron/handson-ml2', 0.5303460955619812, 'ml', 0), ('python/cpython', 0.5265739560127258, 'util', 0), ('ranaroussi/quantstats', 0.5245246887207031, 'finance', 0), 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1,574 | llm | https://github.com/facebookresearch/shepherd | ['language-model', 'critic'] | null | [] | [] | null | null | null | facebookresearch/shepherd | Shepherd | 189 | 9 | 5 | Jupyter Notebook | null | This is the repo for the paper Shepherd -- A Critic for Language Model Generation | facebookresearch | 2024-01-10 | 2023-07-29 | 26 | 7.151351 | https://avatars.githubusercontent.com/u/16943930?v=4 | This is the repo for the paper Shepherd -- A Critic for Language Model Generation | [] | ['critic', 'language-model'] | 2023-08-10 | [('yueyu1030/attrprompt', 0.572687566280365, 'llm', 0), ('openai/finetune-transformer-lm', 0.5617777705192566, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5500012636184692, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5445735454559326, 'llm', 1), ('huggingface/text-generation-inference', 0.5433861017227173, 'llm', 0), ('ai21labs/lm-evaluation', 0.5377524495124817, 'llm', 1), ('lvwerra/trl', 0.5294312834739685, 'llm', 0), ('hannibal046/awesome-llm', 0.5266714692115784, 'study', 1), ('neulab/prompt2model', 0.52313631772995, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5185016393661499, 'llm', 1), ('guidance-ai/guidance', 0.5177493095397949, 'llm', 1), ('lianjiatech/belle', 0.5161774158477783, 'llm', 0), ('openai/gpt-2', 0.5036864280700684, 'llm', 0), ('juncongmoo/pyllama', 0.5019182562828064, 'llm', 0), ('reasoning-machines/pal', 0.5001876950263977, 'llm', 1)] | 4 | 3 | null | 0.1 | 0 | 0 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 24 |
1,715 | security | https://github.com/aswinnnn/pyscan | ['code-quality'] | null | [] | [] | null | null | null | aswinnnn/pyscan | pyscan | 171 | 4 | 4 | Rust | null | python dependency vulnerability scanner, written in Rust. | aswinnnn | 2024-01-09 | 2023-05-16 | 37 | 4.621622 | null | python dependency vulnerability scanner, written in Rust. | ['cve', 'hacking', 'ossf', 'osv', 'rust', 'security', 'security-audit', 'security-automation', 'security-tools', 'vulnerabilities', 'vulnerability', 'vulnerability-scanners'] | ['code-quality', 'cve', 'hacking', 'ossf', 'osv', 'rust', 'security', 'security-audit', 'security-automation', 'security-tools', 'vulnerabilities', 'vulnerability', 'vulnerability-scanners'] | 2023-10-29 | [('astral-sh/ruff', 0.6466583609580994, 'util', 2), ('rustpython/rustpython', 0.6372954249382019, 'util', 1), ('pyupio/safety', 0.6276425123214722, 'security', 2), ('trailofbits/pip-audit', 0.6042621731758118, 'security', 2), ('pyo3/maturin', 0.552875280380249, 'util', 1), ('facebook/pyre-check', 0.5478062033653259, 'typing', 2), ('pola-rs/polars', 0.5435234308242798, 'pandas', 1), ('aquasecurity/trivy', 0.5372655987739563, 'security', 4), ('pycqa/bandit', 0.5267046093940735, 'security', 3), ('nedbat/coveragepy', 0.525583028793335, 'testing', 0), ('pyca/cryptography', 0.5230307579040527, 'util', 0), ('rubik/radon', 0.5182473659515381, 'util', 0), ('python-odin/odin', 0.5133723616600037, 'util', 0), ('mdmzfzl/neetcode-solutions', 0.5120133757591248, 'study', 1), ('pyo3/rust-numpy', 0.5087146759033203, 'util', 1), ('tiiuae/sbomnix', 0.5072173476219177, 'util', 2), ('ta-lib/ta-lib-python', 0.501020610332489, 'finance', 0), ('google/pytype', 0.5005221366882324, 'typing', 1)] | 2 | 1 | null | 2.44 | 1 | 0 | 8 | 3 | 4 | 9 | 4 | 1 | 0 | 90 | 0 | 24 |
601 | web | https://github.com/pyscript/pyscript-cli | [] | null | [] | [] | null | null | null | pyscript/pyscript-cli | pyscript-cli | 156 | 19 | 13 | Python | null | A CLI for PyScript | pyscript | 2024-01-04 | 2022-05-01 | 91 | 1.70892 | https://avatars.githubusercontent.com/u/100553281?v=4 | A CLI for PyScript | [] | [] | 2023-08-17 | [('tiangolo/typer', 0.6438218355178833, 'term', 0), ('google/python-fire', 0.6401512026786804, 'term', 0), ('pyscript/pyscript', 0.606472909450531, 'web', 0), ('python/cpython', 0.5967158079147339, 'util', 0), ('kellyjonbrazil/jc', 0.5959556102752686, 'util', 0), ('pytoolz/toolz', 0.5934836864471436, 'util', 0), ('python-poetry/cleo', 0.5874193906784058, 'term', 0), ('pypy/pypy', 0.5749022364616394, 'util', 0), ('urwid/urwid', 0.5667005181312561, 'term', 0), ('hoffstadt/dearpygui', 0.5643460750579834, 'gui', 0), ('pexpect/pexpect', 0.5519907474517822, 'util', 0), ('renpy/renpy', 0.5380860567092896, 'viz', 0), ('jquast/blessed', 0.5302552580833435, 'term', 0), ('microsoft/playwright-python', 0.5237164497375488, 'testing', 0), ('eleutherai/pyfra', 0.5200616717338562, 'ml', 0), ('willmcgugan/textual', 0.5197693109512329, 'term', 0), ('textualize/trogon', 0.516979992389679, 'term', 0), ('pyston/pyston', 0.5076680183410645, 'util', 0), ('pygamelib/pygamelib', 0.5070560574531555, 'gamedev', 0), ('prompt-toolkit/ptpython', 0.5022722482681274, 'util', 0)] | 10 | 3 | null | 0.83 | 2 | 0 | 21 | 5 | 0 | 3 | 3 | 2 | 2 | 90 | 1 | 24 |
1,410 | math | https://github.com/lean-dojo/reprover | ['retrieval-augmentation'] | null | [] | [] | null | null | null | lean-dojo/reprover | ReProver | 134 | 19 | 9 | Python | https://leandojo.org | Retrieval-Augmented Theorem Provers for Lean | lean-dojo | 2024-01-09 | 2023-03-16 | 45 | 2.93125 | https://avatars.githubusercontent.com/u/136513911?v=4 | Retrieval-Augmented Theorem Provers for Lean | ['lean', 'machine-learning', 'theorem-proving'] | ['lean', 'machine-learning', 'retrieval-augmentation', 'theorem-proving'] | 2023-12-26 | [('lean-dojo/leandojo', 0.6379106640815735, 'math', 3)] | 6 | 1 | null | 1.02 | 11 | 9 | 10 | 1 | 0 | 0 | 0 | 11 | 8 | 90 | 0.7 | 24 |
579 | gis | https://github.com/developmentseed/cogeo-mosaic | [] | null | [] | [] | null | null | null | developmentseed/cogeo-mosaic | cogeo-mosaic | 92 | 25 | 8 | Python | https://developmentseed.org/cogeo-mosaic/ | Create and use COG mosaic based on mosaicJSON | developmentseed | 2023-12-12 | 2019-05-14 | 246 | 0.373984 | https://avatars.githubusercontent.com/u/92384?v=4 | Create and use COG mosaic based on mosaicJSON | [] | [] | 2023-12-06 | [] | 11 | 7 | null | 0.48 | 2 | 2 | 57 | 1 | 0 | 10 | 10 | 2 | 0 | 90 | 0 | 24 |
1,676 | util | https://github.com/lyz-code/autoimport | [] | null | [] | [] | null | null | null | lyz-code/autoimport | autoimport | 80 | 25 | 2 | Python | https://lyz-code.github.io/autoimport | Autoimport automatically fixes wrong import statements. | lyz-code | 2024-01-12 | 2020-10-16 | 171 | 0.466278 | null | Autoimport automatically fixes wrong import statements. | [] | [] | 2023-11-23 | [] | 15 | 3 | null | 0.25 | 5 | 2 | 39 | 2 | 0 | 13 | 13 | 5 | 4 | 90 | 0.8 | 24 |
1,714 | ml | https://github.com/dylanhogg/llmgraph | [] | null | [] | [] | null | null | null | dylanhogg/llmgraph | llmgraph | 22 | 2 | 4 | Python | null | Create knowledge graphs with LLMs | dylanhogg | 2024-01-14 | 2023-10-05 | 16 | 1.316239 | null | Create knowledge graphs with LLMs | ['chatgpt', 'gephi', 'knowledge-graph', 'large-language-model', 'llama2', 'llm'] | ['chatgpt', 'gephi', 'knowledge-graph', 'large-language-model', 'llama2', 'llm'] | 2024-01-04 | [('mooler0410/llmspracticalguide', 0.6462394595146179, 'study', 0), ('nomic-ai/gpt4all', 0.6398856043815613, 'llm', 0), ('microsoft/autogen', 0.6262103915214539, 'llm', 1), ('awslabs/dgl-ke', 0.6202223300933838, 'ml', 1), ('hwchase17/langchain', 0.6144862174987793, 'llm', 0), ('spcl/graph-of-thoughts', 0.6043452024459839, 'llm', 1), ('young-geng/easylm', 0.6026452779769897, 'llm', 0), ('run-llama/rags', 0.5997524857521057, 'llm', 2), ('zilliztech/gptcache', 0.5875337719917297, 'llm', 2), ('shishirpatil/gorilla', 0.5875014662742615, 'llm', 2), ('deepset-ai/haystack', 0.5867999792098999, 'llm', 1), ('langchain-ai/langgraph', 0.5856835842132568, 'llm', 0), ('bobazooba/xllm', 0.5852743983268738, 'llm', 3), ('explosion/spacy-llm', 0.5829962491989136, 'llm', 1), ('zjunlp/deepke', 0.5825223922729492, 'ml', 1), ('confident-ai/deepeval', 0.581521213054657, 'testing', 2), ('accenture/ampligraph', 0.5810795426368713, 'data', 1), ('thudm/chatglm2-6b', 0.580618679523468, 'llm', 1), ('lupantech/chameleon-llm', 0.574148416519165, 'llm', 2), ('salesforce/xgen', 0.5694348812103271, 'llm', 1), ('lianjiatech/belle', 0.5655115842819214, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5651019811630249, 'study', 1), ('eth-sri/lmql', 0.5644213557243347, 'llm', 1), ('salesforce/codet5', 0.563266396522522, 'nlp', 0), ('intel/intel-extension-for-transformers', 0.5522487163543701, 'perf', 1), ('guidance-ai/guidance', 0.5485440492630005, 'llm', 1), ('deepgraphlearning/ultra', 0.5485401749610901, 'ml', 1), ('embedchain/embedchain', 0.5462237596511841, 'llm', 2), ('hiyouga/llama-factory', 0.5430908203125, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5430907607078552, 'llm', 1), ('next-gpt/next-gpt', 0.5404556393623352, 'llm', 2), ('juncongmoo/pyllama', 0.5386449694633484, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5368094444274902, 'llm', 1), ('mlc-ai/web-llm', 0.5363897085189819, 'llm', 2), ('hannibal046/awesome-llm', 0.5356044769287109, 'study', 0), ('hegelai/prompttools', 0.5341759324073792, 'llm', 0), ('eugeneyan/open-llms', 0.5341588258743286, 'study', 1), ('llmware-ai/llmware', 0.5296906232833862, 'llm', 0), ('rcgai/simplyretrieve', 0.5281891226768494, 'llm', 0), ('cg123/mergekit', 0.5279366970062256, 'llm', 1), ('eleutherai/the-pile', 0.5278661847114563, 'data', 1), ('nebuly-ai/nebullvm', 0.527693510055542, 'perf', 1), ('microsoft/jarvis', 0.5276885032653809, 'llm', 0), ('li-plus/chatglm.cpp', 0.524939239025116, 'llm', 0), ('fasteval/fasteval', 0.5239781141281128, 'llm', 1), ('nicolas-hbt/pygraft', 0.5217655897140503, 'ml', 1), ('microsoft/promptcraft-robotics', 0.5207331776618958, 'sim', 2), ('epfllm/meditron', 0.5147185921669006, 'llm', 0), ('argilla-io/argilla', 0.5142863988876343, 'nlp', 1), ('microsoft/torchscale', 0.5129197835922241, 'llm', 0), ('oobabooga/text-generation-webui', 0.5120260715484619, 'llm', 0), ('night-chen/toolqa', 0.5115826725959778, 'llm', 0), ('mindsdb/mindsdb', 0.5112169981002808, 'data', 1), ('lm-sys/fastchat', 0.5098823308944702, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5098133087158203, 'llm', 3), ('xtekky/gpt4free', 0.509717583656311, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5081648826599121, 'llm', 0), ('bigscience-workshop/petals', 0.5068194270133972, 'data', 1), ('neuml/txtai', 0.5045521259307861, 'nlp', 1), ('microsoft/promptflow', 0.5038095712661743, 'llm', 2), ('microsoft/vert-papers', 0.5032587647438049, 'nlp', 0), ('ray-project/ray-llm', 0.5024334192276001, 'llm', 1), ('opengenerativeai/genossgpt', 0.5003108382225037, 'llm', 1)] | 2 | 1 | null | 1.12 | 4 | 3 | 3 | 0 | 0 | 16 | 16 | 4 | 8 | 90 | 2 | 24 |
1,314 | study | https://github.com/trananhkma/fucking-awesome-python | ['awesome', 'python'] | null | [] | ['<hide>'] | null | null | null | trananhkma/fucking-awesome-python | fucking-awesome-python | 1,941 | 297 | 72 | null | null | awesome-python with :octocat: :star: and :fork_and_knife: | trananhkma | 2024-01-13 | 2015-09-19 | 436 | 4.447463 | null | awesome-python with :octocat: ⭐ and 🍴 | [] | ['awesome'] | 2023-07-16 | [('dylanhogg/awesome-python', 0.5963839888572693, 'study', 1), ('timofurrer/awesome-asyncio', 0.5646189451217651, 'study', 1), ('carpedm20/emoji', 0.5481189489364624, 'util', 0)] | 7 | 1 | null | 0.1 | 0 | 0 | 101 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 23 |
701 | util | https://github.com/brandon-rhodes/python-patterns | [] | null | [] | [] | null | null | null | brandon-rhodes/python-patterns | python-patterns | 1,203 | 140 | 312 | Python | null | Source code behind the python-patterns.guide site by Brandon Rhodes | brandon-rhodes | 2024-01-13 | 2018-01-31 | 312 | 3.845205 | null | Source code behind the python-patterns.guide site by Brandon Rhodes | [] | [] | 2023-12-27 | [('faif/python-patterns', 0.6763890385627747, 'util', 0), ('gerdm/prml', 0.5963156819343567, 'study', 0), ('python/cpython', 0.5899462103843689, 'util', 0), ('amaargiru/pyroad', 0.5854448676109314, 'study', 0), ('landscapeio/prospector', 0.5751134157180786, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.55435711145401, 'study', 0), ('eleutherai/pyfra', 0.5504909157752991, 'ml', 0), ('pytoolz/toolz', 0.5499976277351379, 'util', 0), ('realpython/python-guide', 0.5476312637329102, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5350989699363708, 'study', 0), ('wesm/pydata-book', 0.5279967188835144, 'study', 0), ('ta-lib/ta-lib-python', 0.5266119837760925, 'finance', 0), ('google/latexify_py', 0.5185614824295044, 'util', 0), ('hhatto/autopep8', 0.5134424567222595, 'util', 0), ('sympy/sympy', 0.5133131742477417, 'math', 0), ('sqlalchemy/mako', 0.5129835605621338, 'template', 0), ('probml/pyprobml', 0.5112978219985962, 'ml', 0), ('nedbat/coveragepy', 0.5084773302078247, 'testing', 0), ('xrudelis/pytrait', 0.5037622451782227, 'util', 0)] | 4 | 1 | null | 0.02 | 0 | 0 | 72 | 1 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 23 |
638 | util | https://github.com/metachris/logzero | [] | null | [] | [] | null | null | null | metachris/logzero | logzero | 1,031 | 76 | 26 | Python | https://logzero.readthedocs.io | Robust and effective logging for Python 2 and 3. | metachris | 2024-01-04 | 2017-06-12 | 346 | 2.978539 | null | Robust and effective logging for Python 2 and 3. | ['logfiles', 'logging', 'logzero'] | ['logfiles', 'logging', 'logzero'] | 2021-03-17 | [('delgan/loguru', 0.7877286672592163, 'util', 1), ('alexmojaki/snoop', 0.601128876209259, 'debug', 1), ('salesforce/logai', 0.5321155786514282, 'util', 0)] | 12 | 5 | null | 0 | 0 | 0 | 80 | 34 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 23 |
270 | crypto | https://github.com/man-c/pycoingecko | [] | null | [] | [] | null | null | null | man-c/pycoingecko | pycoingecko | 1,008 | 262 | 31 | Python | null | Python wrapper for the CoinGecko API | man-c | 2024-01-12 | 2018-08-24 | 283 | 3.55466 | null | Python wrapper for the CoinGecko API | ['api', 'api-wrapper', 'coingecko', 'crypto', 'cryptocurrency', 'nft', 'nfts', 'wrapper'] | ['api', 'api-wrapper', 'coingecko', 'crypto', 'cryptocurrency', 'nft', 'nfts', 'wrapper'] | 2022-10-26 | [('1200wd/bitcoinlib', 0.6038165092468262, 'crypto', 0), ('legrandin/pycryptodome', 0.5711352229118347, 'util', 0), ('pyca/cryptography', 0.5643881559371948, 'util', 0), ('ethereum/web3.py', 0.5624127388000488, 'crypto', 0), ('lukasschwab/arxiv.py', 0.5484018921852112, 'util', 0), ('ta-lib/ta-lib-python', 0.5362427234649658, 'finance', 0), ('pyca/pynacl', 0.5348667502403259, 'util', 0), ('openai/openai-python', 0.5335339307785034, 'util', 0), ('meilisearch/meilisearch-python', 0.5315601229667664, 'data', 1), ('bottlepy/bottle', 0.5294601917266846, 'web', 0), ('primal100/pybitcointools', 0.5288224220275879, 'crypto', 0), ('ccxt/ccxt', 0.5272769927978516, 'crypto', 3), ('cuemacro/findatapy', 0.5231452584266663, 'finance', 0), ('pytoolz/toolz', 0.5226123929023743, 'util', 0), ('masoniteframework/masonite', 0.5191899538040161, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5065068602561951, 'data', 1)] | 14 | 1 | null | 0 | 1 | 0 | 66 | 15 | 0 | 4 | 4 | 1 | 0 | 90 | 0 | 23 |
1,876 | sim | https://github.com/hardmaru/estool | [] | null | [] | [] | null | null | null | hardmaru/estool | estool | 913 | 162 | 33 | Jupyter Notebook | null | Evolution Strategies Tool | hardmaru | 2024-01-04 | 2017-10-29 | 326 | 2.798161 | null | Evolution Strategies Tool | [] | [] | 2022-01-20 | [] | 9 | 2 | null | 0 | 1 | 1 | 76 | 24 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 23 |
161 | nlp | https://github.com/nipunsadvilkar/pysbd | [] | null | [] | [] | null | null | null | nipunsadvilkar/pysbd | pySBD | 690 | 76 | 13 | Python | null | 🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box. | nipunsadvilkar | 2024-01-13 | 2017-06-11 | 346 | 1.992574 | null | 🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box. | ['rule-based', 'segmentation', 'sentence', 'sentence-boundary-detection', 'sentence-tokenizer'] | ['rule-based', 'segmentation', 'sentence', 'sentence-boundary-detection', 'sentence-tokenizer'] | 2021-02-11 | [('pemistahl/lingua-py', 0.5244992971420288, 'nlp', 0)] | 7 | 2 | null | 0 | 2 | 1 | 80 | 36 | 0 | 2 | 2 | 2 | 3 | 90 | 1.5 | 23 |
745 | diffusion | https://github.com/sharonzhou/long_stable_diffusion | [] | null | [] | [] | null | null | null | sharonzhou/long_stable_diffusion | long_stable_diffusion | 671 | 55 | 16 | Python | null | Long-form text-to-images generation, using a pipeline of deep generative models (GPT-3 and Stable Diffusion) | sharonzhou | 2024-01-12 | 2022-09-04 | 73 | 9.155945 | null | Long-form text-to-images generation, using a pipeline of deep generative models (GPT-3 and Stable Diffusion) | [] | [] | 2022-10-30 | [('compvis/stable-diffusion', 0.6926607489585876, 'diffusion', 0), ('huggingface/diffusers', 0.6350945830345154, 'diffusion', 0), ('openai/glide-text2im', 0.6345992684364319, 'diffusion', 0), ('saharmor/dalle-playground', 0.6195039749145508, 'diffusion', 0), ('thudm/cogvideo', 0.6085971593856812, 'ml', 0), ('huggingface/text-generation-inference', 0.5840281844139099, 'llm', 0), ('compvis/latent-diffusion', 0.5753589272499084, 'diffusion', 0), ('stability-ai/stablediffusion', 0.5753588676452637, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5584306120872498, 'diffusion', 0), ('minimaxir/gpt-2-simple', 0.5567746758460999, 'llm', 0), ('google/sentencepiece', 0.5528421401977539, 'nlp', 0), ('lucidrains/deep-daze', 0.5463470220565796, 'ml', 0), ('yoadtew/zero-shot-image-to-text', 0.5378433465957642, 'nlp', 0), ('ashawkey/stable-dreamfusion', 0.5333707332611084, 'diffusion', 0), ('minimaxir/aitextgen', 0.5227047801017761, 'llm', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.513484537601471, 'web', 0), ('lucidrains/imagen-pytorch', 0.5128797292709351, 'ml-dl', 0), ('nateraw/stable-diffusion-videos', 0.512672483921051, 'diffusion', 0), ('davidadsp/generative_deep_learning_2nd_edition', 0.5068796277046204, 'study', 0), ('nv-tlabs/get3d', 0.5064239501953125, 'ml', 0), ('google-research/electra', 0.5007908344268799, 'ml-dl', 0)] | 2 | 1 | null | 0 | 0 | 0 | 17 | 15 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 23 |
969 | diffusion | https://github.com/tanelp/tiny-diffusion | [] | null | [] | [] | null | null | null | tanelp/tiny-diffusion | tiny-diffusion | 483 | 43 | 8 | Jupyter Notebook | null | A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets. | tanelp | 2024-01-12 | 2023-01-13 | 54 | 8.850785 | null | A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets. | [] | [] | 2023-02-19 | [('divamgupta/stable-diffusion-tensorflow', 0.6014887690544128, 'diffusion', 0), ('huggingface/diffusers', 0.5994593501091003, 'diffusion', 0), ('openai/improved-diffusion', 0.5728610754013062, 'diffusion', 0), ('pytorch/botorch', 0.5327487587928772, 'ml-dl', 0), ('comfyanonymous/comfyui', 0.5260990858078003, 'diffusion', 0), ('openai/point-e', 0.5226495265960693, 'util', 0), ('carson-katri/dream-textures', 0.5146340727806091, 'diffusion', 0), ('bentoml/onediffusion', 0.5116053223609924, 'diffusion', 0)] | 1 | 0 | null | 0.06 | 3 | 0 | 12 | 11 | 0 | 0 | 0 | 3 | 2 | 90 | 0.7 | 23 |
1,374 | llm | https://github.com/amazon-science/alexa-teacher-models | ['aws', 'language-model', 'sagemaker'] | AlexaTM 20B is a 20B-Parameter sequence-to-sequence transformer model | [] | [] | null | null | null | amazon-science/alexa-teacher-models | alexa-teacher-models | 362 | 26 | 36 | Python | null | null | amazon-science | 2024-01-04 | 2022-08-04 | 77 | 4.658088 | https://avatars.githubusercontent.com/u/70298811?v=4 | AlexaTM 20B is a 20B-Parameter sequence-to-sequence transformer model | [] | ['aws', 'language-model', 'sagemaker'] | 2023-04-09 | [] | 5 | 2 | null | 0.15 | 0 | 0 | 18 | 9 | 2 | 1 | 2 | 0 | 0 | 90 | 0 | 23 |
862 | ml | https://github.com/infer-actively/pymdp | [] | null | [] | [] | null | null | null | infer-actively/pymdp | pymdp | 347 | 56 | 30 | Python | null | A Python implementation of active inference for Markov Decision Processes | infer-actively | 2024-01-11 | 2019-11-27 | 217 | 1.592787 | https://avatars.githubusercontent.com/u/75545318?v=4 | A Python implementation of active inference for Markov Decision Processes | [] | [] | 2023-08-19 | [('pymc-devs/pymc3', 0.5866931080818176, 'ml', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5399729013442993, 'study', 0), ('guyallard/markov_clustering', 0.5361882448196411, 'graph', 0), ('artemyk/dynpy', 0.5208140015602112, 'sim', 0)] | 14 | 2 | null | 0.33 | 4 | 2 | 50 | 5 | 1 | 1 | 1 | 4 | 2 | 90 | 0.5 | 23 |
1,301 | data | https://github.com/mattbierbaum/arxiv-public-datasets | [] | null | [] | [] | null | null | null | mattbierbaum/arxiv-public-datasets | arxiv-public-datasets | 338 | 55 | 15 | Python | https://arxiv.org/abs/1905.00075 | A set of scripts to grab public datasets from resources related to arXiv | mattbierbaum | 2024-01-11 | 2019-01-29 | 261 | 1.295019 | null | A set of scripts to grab public datasets from resources related to arXiv | [] | [] | 2022-07-15 | [('mcordts/cityscapesscripts', 0.5378220677375793, 'gis', 0), ('jovianml/opendatasets', 0.5266126990318298, 'data', 0), ('lukasschwab/arxiv.py', 0.5195563435554504, 'util', 0), ('simonw/datasette', 0.5164351463317871, 'data', 0)] | 9 | 3 | null | 0 | 2 | 2 | 60 | 18 | 0 | 1 | 1 | 2 | 4 | 90 | 2 | 23 |
991 | finance | https://github.com/nasdaq/data-link-python | [] | null | [] | [] | null | null | null | nasdaq/data-link-python | data-link-python | 333 | 59 | 10 | Python | null | A Python library for Nasdaq Data Link's RESTful API | nasdaq | 2024-01-05 | 2021-11-02 | 117 | 2.846154 | https://avatars.githubusercontent.com/u/13860626?v=4 | A Python library for Nasdaq Data Link's RESTful API | [] | [] | 2022-08-29 | [('pynamodb/pynamodb', 0.5948277711868286, 'data', 0), ('hydrosquall/tiingo-python', 0.5673314332962036, 'finance', 0), ('tiangolo/sqlmodel', 0.5559763312339783, 'data', 0), ('falconry/falcon', 0.5472946763038635, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5415635704994202, 'data', 0), ('cuemacro/findatapy', 0.538777232170105, 'finance', 0), ('airbnb/omniduct', 0.5350242853164673, 'data', 0), ('mcfunley/pugsql', 0.5342159867286682, 'data', 0), ('datastax/python-driver', 0.5312590599060059, 'data', 0), ('awslabs/python-deequ', 0.5292037725448608, 'ml', 0), ('openaddresses/pyesridump', 0.5270365476608276, 'gis', 0), ('openai/openai-python', 0.5095574855804443, 'util', 0), ('sqlalchemy/sqlalchemy', 0.5085864067077637, 'data', 0), ('encode/httpx', 0.5050049424171448, 'web', 0), ('ethereum/web3.py', 0.5034674406051636, 'crypto', 0), ('taverntesting/tavern', 0.5025106072425842, 'testing', 0)] | 4 | 1 | null | 0 | 3 | 0 | 27 | 17 | 0 | 2 | 2 | 3 | 6 | 90 | 2 | 23 |
1,461 | jupyter | https://github.com/mamba-org/gator | ['conda', 'packages'] | null | [] | [] | null | null | null | mamba-org/gator | gator | 252 | 29 | 6 | TypeScript | null | Conda environment and package management extension from within Jupyter | mamba-org | 2024-01-04 | 2018-08-02 | 286 | 0.878924 | https://avatars.githubusercontent.com/u/66118895?v=4 | Conda environment and package management extension from within Jupyter | ['conda', 'jupyter-notebook', 'jupyterlab-extension'] | ['conda', 'jupyter-notebook', 'jupyterlab-extension', 'packages'] | 2023-10-26 | [('conda/conda-pack', 0.6601476073265076, 'util', 1), ('conda/conda-build', 0.6426661014556885, 'util', 1), ('jupyter-widgets/ipywidgets', 0.6345846056938171, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.6041745543479919, 'jupyter', 0), ('chaoleili/jupyterlab_tensorboard', 0.5936612486839294, 'jupyter', 1), ('jupyter/notebook', 0.5904126763343811, 'jupyter', 1), ('mamba-org/quetz', 0.5806750655174255, 'util', 2), ('conda/constructor', 0.5621293783187866, 'util', 1), ('jupyterlab/jupyterlab-desktop', 0.5549662709236145, 'jupyter', 1), ('pypa/flit', 0.5534806847572327, 'util', 0), ('jupyterlite/jupyterlite', 0.5473883152008057, 'jupyter', 1), ('voila-dashboards/voila', 0.5456347465515137, 'jupyter', 2), ('aws/graph-notebook', 0.5385718941688538, 'jupyter', 1), ('pypa/hatch', 0.5378934741020203, 'util', 0), ('mwouts/jupytext', 0.5371402502059937, 'jupyter', 2), ('indygreg/pyoxidizer', 0.5344242453575134, 'util', 0), ('mitsuhiko/rye', 0.5343549847602844, 'util', 0), ('mamba-org/boa', 0.5324650406837463, 'util', 1), ('jupyter/nbformat', 0.5302620530128479, 'jupyter', 0), ('python-poetry/poetry', 0.5295340418815613, 'util', 0), ('ipython/ipykernel', 0.5081201791763306, 'util', 1), ('mamba-org/mamba', 0.5042513012886047, 'util', 1)] | 26 | 4 | null | 0.13 | 12 | 7 | 66 | 3 | 1 | 11 | 1 | 12 | 3 | 90 | 0.2 | 23 |
829 | gis | https://github.com/r-barnes/richdem | [] | null | [] | [] | null | null | null | r-barnes/richdem | richdem | 234 | 62 | 14 | C++ | null | High-performance Terrain and Hydrology Analysis | r-barnes | 2024-01-03 | 2013-01-06 | 577 | 0.405345 | null | High-performance Terrain and Hydrology Analysis | ['big-data', 'digital-elevation-model', 'geosciences', 'geospatial', 'hydrologic-modeling', 'hydrology'] | ['big-data', 'digital-elevation-model', 'geosciences', 'geospatial', 'hydrologic-modeling', 'hydrology'] | 2023-07-06 | [('fatiando/verde', 0.5110668540000916, 'gis', 1), ('osgeo/grass', 0.5100005269050598, 'gis', 1)] | 4 | 3 | null | 0.21 | 6 | 0 | 134 | 6 | 0 | 2 | 2 | 6 | 15 | 90 | 2.5 | 23 |
1,338 | util | https://github.com/prefecthq/server | [] | null | [] | [] | null | null | null | prefecthq/server | server | 223 | 98 | 16 | Python | null | The Prefect API and backend | prefecthq | 2024-01-14 | 2020-07-29 | 182 | 1.219531 | https://avatars.githubusercontent.com/u/39270919?v=4 | The Prefect API and backend | ['automation', 'orchestration', 'prefect', 'workflow', 'workflow-engine'] | ['automation', 'orchestration', 'prefect', 'workflow', 'workflow-engine'] | 2023-06-23 | [('prefecthq/prefect', 0.7347409129142761, 'ml-ops', 5), ('kestra-io/kestra', 0.6529881954193115, 'ml-ops', 3), ('flyteorg/flyte', 0.6038605570793152, 'ml-ops', 1), ('avaiga/taipy', 0.5884959697723389, 'data', 3), ('apache/airflow', 0.5666928291320801, 'ml-ops', 4), ('tiangolo/full-stack-fastapi-postgresql', 0.5580776333808899, 'template', 0), ('zenml-io/zenml', 0.5477404594421387, 'ml-ops', 1), ('astronomer/astro-sdk', 0.5464804768562317, 'ml-ops', 0), ('dagster-io/dagster', 0.5430384278297424, 'ml-ops', 2), ('vitalik/django-ninja', 0.5366072654724121, 'web', 0), ('piccolo-orm/piccolo_admin', 0.5307790040969849, 'data', 0), ('tiangolo/fastapi', 0.5276178121566772, 'web', 0), ('fastai/ghapi', 0.5246346592903137, 'util', 0), ('mage-ai/mage-ai', 0.5205321311950684, 'ml-ops', 1), ('willmcgugan/textual', 0.5155190229415894, 'term', 0), ('cheshire-cat-ai/core', 0.5153402090072632, 'llm', 0), ('home-assistant/supervisor', 0.5141927599906921, 'util', 0), ('hugapi/hug', 0.5119301676750183, 'util', 0), ('starlite-api/starlite', 0.5044628381729126, 'web', 0), ('numerai/numerai-cli', 0.5039493441581726, 'finance', 0), ('orchest/orchest', 0.5037789344787598, 'ml-ops', 0), ('pythagora-io/gpt-pilot', 0.500454843044281, 'llm', 0), ('ploomber/ploomber', 0.5003852248191833, 'ml-ops', 1)] | 48 | 4 | null | 0.06 | 0 | 0 | 42 | 7 | 0 | 14 | 14 | 0 | 0 | 90 | 0 | 23 |
1,609 | llm | https://github.com/hazyresearch/legalbench | ['benchmark', 'legal'] | null | [] | [] | null | null | null | hazyresearch/legalbench | legalbench | 220 | 23 | 40 | Python | null | An open science effort to benchmark legal reasoning in foundation models | hazyresearch | 2024-01-14 | 2022-08-19 | 75 | 2.911153 | https://avatars.githubusercontent.com/u/2165246?v=4 | An open science effort to benchmark legal reasoning in foundation models | [] | ['benchmark', 'legal'] | 2023-12-02 | [('coastalcph/lex-glue', 0.5063652992248535, 'nlp', 2)] | 4 | 1 | null | 1.46 | 3 | 1 | 17 | 1 | 0 | 0 | 0 | 3 | 2 | 90 | 0.7 | 23 |
1,694 | util | https://github.com/regebro/pyroma | ['quality'] | null | [] | [] | null | null | null | regebro/pyroma | pyroma | 201 | 24 | 7 | Python | null | Rate your Python packages package friendliness | regebro | 2023-12-11 | 2017-04-15 | 354 | 0.56711 | null | Rate your Python packages package friendliness | ['packaging'] | ['packaging', 'quality'] | 2023-10-10 | [('pypa/flit', 0.7084984183311462, 'util', 1), ('indygreg/pyoxidizer', 0.7064893245697021, 'util', 1), ('python-poetry/poetry', 0.6802058219909668, 'util', 1), ('mitsuhiko/rye', 0.661931574344635, 'util', 1), ('pypi/warehouse', 0.6017106771469116, 'util', 0), ('tezromach/python-package-template', 0.5707094073295593, 'template', 0), ('pdm-project/pdm', 0.568504810333252, 'util', 1), ('pyodide/micropip', 0.5652766227722168, 'util', 0), ('pypa/hatch', 0.5634012818336487, 'util', 1), ('rubik/radon', 0.5549668669700623, 'util', 0)] | 21 | 6 | null | 0.15 | 1 | 0 | 82 | 3 | 0 | 7 | 7 | 1 | 0 | 90 | 0 | 23 |
1,771 | sim | https://github.com/humanoidagents/humanoidagents | ['simulation', 'chatgpt', 'agents', 'language-model'] | null | [] | [] | null | null | null | humanoidagents/humanoidagents | HumanoidAgents | 197 | 20 | 5 | Python | http://www.humanoidagents.com/ | Humanoid Agents: Platform for Simulating Human-like Generative Agents | humanoidagents | 2024-01-14 | 2023-10-09 | 16 | 12.20354 | https://avatars.githubusercontent.com/u/147342724?v=4 | Humanoid Agents: Platform for Simulating Human-like Generative Agents | [] | ['agents', 'chatgpt', 'language-model', 'simulation'] | 2023-10-19 | [('google-deepmind/concordia', 0.640636682510376, 'sim', 0), ('minedojo/voyager', 0.56615149974823, 'llm', 0), ('prefecthq/marvin', 0.5612106919288635, 'nlp', 1), ('microsoft/autogen', 0.5427013039588928, 'llm', 1), ('aiwaves-cn/agents', 0.5400265455245972, 'nlp', 1), ('projectmesa/mesa', 0.5313592553138733, 'sim', 1), ('facebookresearch/droidlet', 0.5291570425033569, 'sim', 0), ('facebookresearch/habitat-lab', 0.5243606567382812, 'sim', 0), ('krohling/bondai', 0.5202670693397522, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5163865685462952, 'llm', 0), ('microsoft/promptcraft-robotics', 0.51203453540802, 'sim', 2), ('mnotgod96/appagent', 0.5099534392356873, 'llm', 1), ('activitysim/populationsim', 0.5014925003051758, 'sim', 0), ('noahshinn/reflexion', 0.5013625621795654, 'llm', 0)] | 1 | 0 | null | 0.12 | 2 | 1 | 3 | 3 | 0 | 0 | 0 | 2 | 0 | 90 | 0 | 23 |
768 | sim | https://github.com/activitysim/activitysim | [] | null | [] | [] | null | null | null | activitysim/activitysim | activitysim | 176 | 92 | 44 | Jupyter Notebook | https://activitysim.github.io | An Open Platform for Activity-Based Travel Modeling | activitysim | 2024-01-03 | 2014-06-18 | 501 | 0.350697 | https://avatars.githubusercontent.com/u/25851945?v=4 | An Open Platform for Activity-Based Travel Modeling | ['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'travel-modeling'] | ['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'travel-modeling'] | 2023-05-09 | [] | 32 | 4 | null | 0.23 | 41 | 9 | 117 | 8 | 3 | 2 | 3 | 41 | 22 | 90 | 0.5 | 23 |
828 | typing | https://github.com/jellezijlstra/autotyping | [] | null | [] | [] | null | null | null | jellezijlstra/autotyping | autotyping | 175 | 12 | 5 | Python | null | null | jellezijlstra | 2024-01-04 | 2021-06-25 | 135 | 1.290832 | null | jellezijlstra/autotyping | [] | [] | 2023-03-28 | [] | 8 | 6 | null | 0.08 | 1 | 0 | 31 | 10 | 2 | 2 | 2 | 1 | 1 | 90 | 1 | 23 |
1,664 | util | https://github.com/python-poetry/install.python-poetry.org | [] | null | [] | [] | null | null | null | python-poetry/install.python-poetry.org | install.python-poetry.org | 157 | 50 | 8 | Python | https://install.python-poetry.org | The official Poetry installation script | python-poetry | 2024-01-09 | 2021-11-12 | 115 | 1.358467 | https://avatars.githubusercontent.com/u/48722593?v=4 | The official Poetry installation script | ['poetry'] | ['poetry'] | 2023-09-19 | [('snok/install-poetry', 0.6302388310432434, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5517739653587341, 'util', 0), ('mtkennerly/poetry-dynamic-versioning', 0.5113168358802795, 'util', 1)] | 17 | 2 | null | 0.27 | 13 | 6 | 26 | 4 | 0 | 0 | 0 | 13 | 21 | 90 | 1.6 | 23 |
1,415 | llm | https://github.com/yueyu1030/attrprompt | [] | null | [] | [] | null | null | null | yueyu1030/attrprompt | AttrPrompt | 107 | 7 | 3 | Python | https://arxiv.org/abs/2306.15895 | [NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`. | yueyu1030 | 2024-01-12 | 2023-05-31 | 34 | 3.069672 | null | [NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`. | ['attributed-text', 'data-centric-ai', 'large-language-models', 'natural-language-processing', 'pretrained-language-model', 'text-classification', 'training-data-generation', 'zero-shot-learning'] | ['attributed-text', 'data-centric-ai', 'large-language-models', 'natural-language-processing', 'pretrained-language-model', 'text-classification', 'training-data-generation', 'zero-shot-learning'] | 2023-11-02 | [('togethercomputer/redpajama-data', 0.6486657857894897, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.6001567840576172, 'llm', 2), ('eleutherai/the-pile', 0.5999411940574646, 'data', 0), ('huggingface/text-generation-inference', 0.5905746221542358, 'llm', 0), ('facebookresearch/shepherd', 0.572687566280365, 'llm', 0), ('microsoft/unilm', 0.5726070404052734, 'nlp', 0), ('openai/gpt-2', 0.5717838406562805, 'llm', 0), ('princeton-nlp/alce', 0.5694684982299805, 'llm', 0), ('lm-sys/fastchat', 0.5685467720031738, 'llm', 0), ('microsoft/lora', 0.5668091177940369, 'llm', 0), ('hannibal046/awesome-llm', 0.5640091896057129, 'study', 0), ('databrickslabs/dolly', 0.5601263642311096, 'llm', 0), ('lupantech/chameleon-llm', 0.5579221248626709, 'llm', 0), ('infinitylogesh/mutate', 0.5573945045471191, 'nlp', 0), ('lianjiatech/belle', 0.5536043047904968, 'llm', 0), ('google-research/electra', 0.5535896420478821, 'ml-dl', 0), ('openai/finetune-transformer-lm', 0.5512840747833252, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5511519312858582, 'llm', 0), ('explosion/spacy-models', 0.5483715534210205, 'nlp', 1), ('minimaxir/textgenrnn', 0.545197069644928, 'nlp', 0), ('openai/clip', 0.5419546961784363, 'ml-dl', 0), ('jonasgeiping/cramming', 0.5370364189147949, 'nlp', 0), ('eleutherai/lm-evaluation-harness', 0.5361191034317017, 'llm', 0), ('alibaba/easynlp', 0.5339798331260681, 'nlp', 1), ('google-research/language', 0.5311223864555359, 'nlp', 1), ('reasoning-machines/pal', 0.5289052128791809, 'llm', 1), ('next-gpt/next-gpt', 0.5283357501029968, 'llm', 1), ('openlm-research/open_llama', 0.5280580520629883, 'llm', 0), ('freedomintelligence/llmzoo', 0.5263165831565857, 'llm', 0), ('bigscience-workshop/biomedical', 0.5249584317207336, 'data', 0), ('nltk/nltk', 0.5240321755409241, 'nlp', 1), ('eureka-research/eureka', 0.522046685218811, 'ml-rl', 0), ('ai21labs/lm-evaluation', 0.5179035067558289, 'llm', 0), ('llmware-ai/llmware', 0.5132665038108826, 'llm', 1), ('ofa-sys/ofa', 0.5103440284729004, 'llm', 0), ('openlmlab/moss', 0.5103434920310974, 'llm', 2), ('thudm/codegeex', 0.510209858417511, 'llm', 0), ('deepset-ai/farm', 0.5037703514099121, 'nlp', 0), ('microsoft/autogen', 0.5033207535743713, 'llm', 0), ('prithivirajdamodaran/styleformer', 0.5027515292167664, 'nlp', 0), ('norskregnesentral/skweak', 0.5007184743881226, 'nlp', 1)] | 3 | 2 | null | 0.44 | 4 | 4 | 8 | 2 | 0 | 0 | 0 | 4 | 3 | 90 | 0.8 | 23 |
1,339 | ml-ops | https://github.com/prefecthq/prefect-dbt | [] | null | [] | [] | null | null | null | prefecthq/prefect-dbt | prefect-dbt | 77 | 8 | 8 | Python | https://prefecthq.github.io/prefect-dbt/ | Collection of Prefect integrations for working with dbt with your Prefect flows. | prefecthq | 2024-01-04 | 2022-05-26 | 87 | 0.87785 | https://avatars.githubusercontent.com/u/39270919?v=4 | Collection of Prefect integrations for working with dbt with your Prefect flows. | ['dbt', 'prefect'] | ['dbt', 'prefect'] | 2023-11-02 | [('prefecthq/prefect', 0.5904790759086609, 'ml-ops', 1), ('prefecthq/prefect-aws', 0.512976348400116, 'data', 1)] | 14 | 4 | null | 0.5 | 11 | 4 | 20 | 2 | 4 | 8 | 4 | 11 | 2 | 90 | 0.2 | 23 |
1,661 | data | https://github.com/mediawiki-client-tools/mediawiki-dump-generator | ['wikimedia', 'wikipedia'] | null | [] | [] | null | null | null | mediawiki-client-tools/mediawiki-dump-generator | mediawiki-dump-generator | 68 | 14 | 5 | HTML | https://github.com/mediawiki-client-tools/mediawiki-scraper | Python 3 tools for downloading and preserving wikis | mediawiki-client-tools | 2024-01-12 | 2021-05-27 | 139 | 0.486708 | https://avatars.githubusercontent.com/u/122663498?v=4 | Python 3 tools for downloading and preserving wikis | [] | ['wikimedia', 'wikipedia'] | 2023-12-18 | [('mediawiki-client-tools/wikitools3', 0.8232905268669128, 'data', 2), ('goldsmith/wikipedia', 0.709117591381073, 'data', 0), ('harangju/wikinet', 0.6572245359420776, 'data', 0), ('executablebooks/jupyter-book', 0.535974383354187, 'jupyter', 0), ('erotemic/ubelt', 0.5286123752593994, 'util', 0)] | 44 | 3 | null | 1.94 | 33 | 22 | 32 | 1 | 0 | 0 | 0 | 33 | 19 | 90 | 0.6 | 23 |
913 | util | https://github.com/python-odin/odin | [] | null | [] | [] | null | null | null | python-odin/odin | odin | 35 | 9 | 3 | Python | https://odin.readthedocs.org/en/latest/ | Data-structure definition/validation/traversal, mapping and serialisation toolkit for Python | python-odin | 2023-11-27 | 2013-08-20 | 545 | 0.06422 | https://avatars.githubusercontent.com/u/14675500?v=4 | Data-structure definition/validation/traversal, mapping and serialisation toolkit for Python | ['csv', 'data-mapping', 'data-structures', 'de-serialize', 'json', 'msgpack', 'serialize', 'validation', 'xml', 'yaml'] | ['csv', 'data-mapping', 'data-structures', 'de-serialize', 'json', 'msgpack', 'serialize', 'validation', 'xml', 'yaml'] | 2024-01-13 | [('marshmallow-code/marshmallow', 0.6529213190078735, 'util', 1), ('pydantic/pydantic', 0.6526608467102051, 'util', 1), ('pandas-dev/pandas', 0.6475261449813843, 'pandas', 0), ('dagworks-inc/hamilton', 0.6443514227867126, 'ml-ops', 0), ('pylons/colander', 0.63074791431427, 'util', 1), ('jsonpickle/jsonpickle', 0.6082868576049805, 'data', 1), ('pyeve/cerberus', 0.6078689098358154, 'data', 0), ('keon/algorithms', 0.6064568161964417, 'util', 0), ('omry/omegaconf', 0.5967031121253967, 'util', 1), ('mkdocstrings/griffe', 0.5799145102500916, 'util', 0), ('lk-geimfari/mimesis', 0.5751269459724426, 'data', 1), ('yukinarit/pyserde', 0.5727947354316711, 'util', 3), ('tiangolo/sqlmodel', 0.5694354176521301, 'data', 1), ('instagram/libcst', 0.5643008351325989, 'util', 0), ('pytoolz/toolz', 0.563791811466217, 'util', 0), ('unionai-oss/pandera', 0.5636017322540283, 'pandas', 1), ('jazzband/tablib', 0.5585665106773376, 'data', 0), ('saulpw/visidata', 0.5557106137275696, 'term', 2), ('plotly/dash', 0.5536272525787354, 'viz', 0), ('fabiocaccamo/python-benedict', 0.5445603728294373, 'util', 4), ('eleutherai/pyfra', 0.5400282144546509, 'ml', 0), ('krzjoa/awesome-python-data-science', 0.5375770330429077, 'study', 0), ('falconry/falcon', 0.5320534706115723, 'web', 0), ('wesm/pydata-book', 0.5320025682449341, 'study', 0), ('malloydata/malloy-py', 0.5316958427429199, 'data', 0), ('roniemartinez/dude', 0.5273708701133728, 'util', 0), ('imageio/imageio', 0.5272129774093628, 'util', 0), ('ploomber/ploomber', 0.5260776877403259, 'ml-ops', 0), ('facebook/pyre-check', 0.5255442261695862, 'typing', 0), ('man-group/dtale', 0.5247855186462402, 'viz', 0), ('atsushisakai/pythonrobotics', 0.5244603753089905, 'sim', 0), ('amaargiru/pyroad', 0.5243930220603943, 'study', 0), ('scikit-mobility/scikit-mobility', 0.5235087275505066, 'gis', 0), ('1200wd/bitcoinlib', 0.5217608213424683, 'crypto', 0), ('fastai/fastcore', 0.5195226669311523, 'util', 1), ('kellyjonbrazil/jc', 0.5191496014595032, 'util', 3), ('uqfoundation/dill', 0.5188406705856323, 'data', 0), ('rhettbull/osxphotos', 0.5156180262565613, 'util', 0), ('brokenloop/jsontopydantic', 0.5140889286994934, 'util', 0), ('geopandas/geopandas', 0.5138459801673889, 'gis', 0), ('aswinnnn/pyscan', 0.5133723616600037, 'security', 0), ('fsspec/filesystem_spec', 0.5112189054489136, 'util', 0), ('pyjanitor-devs/pyjanitor', 0.51002436876297, 'pandas', 0), ('crunch-io/lazycsv', 0.5088388323783875, 'perf', 1), ('erotemic/ubelt', 0.5053083896636963, 'util', 0), ('macbre/sql-metadata', 0.5047857761383057, 'data', 0), ('simonw/datasette', 0.5009411573410034, 'data', 2), ('mitmproxy/pdoc', 0.5004577040672302, 'util', 0)] | 8 | 3 | null | 1.6 | 6 | 3 | 127 | 0 | 10 | 7 | 10 | 6 | 1 | 90 | 0.2 | 23 |
1,284 | data | https://github.com/qdrant/qdrant-haystack | ['haystack'] | null | [] | [] | null | null | null | qdrant/qdrant-haystack | qdrant-haystack | 35 | 9 | 3 | Python | null | An integration of Qdrant ANN vector database backend with Haystack | qdrant | 2023-11-28 | 2023-01-31 | 52 | 0.673077 | https://avatars.githubusercontent.com/u/73504361?v=4 | An integration of Qdrant ANN vector database backend with Haystack | [] | ['haystack'] | 2023-11-14 | [('qdrant/qdrant-client', 0.6654171943664551, 'util', 0), ('qdrant/vector-db-benchmark', 0.5743999481201172, 'perf', 0), ('qdrant/qdrant', 0.5697789788246155, 'data', 0), ('facebookresearch/faiss', 0.518367350101471, 'ml', 0), ('pinecone-io/pinecone-python-client', 0.5180879831314087, 'data', 0), ('jina-ai/vectordb', 0.5077526569366455, 'data', 0)] | 6 | 4 | null | 1.13 | 13 | 4 | 12 | 2 | 15 | 16 | 15 | 13 | 6 | 90 | 0.5 | 23 |
1,762 | term | https://github.com/tconbeer/textual-textarea | ['textual'] | null | [] | [] | null | null | null | tconbeer/textual-textarea | textual-textarea | 10 | 3 | 1 | Python | null | A text area (multi-line input) with syntax highlighting and autocomplete for Textual | tconbeer | 2023-12-25 | 2023-05-19 | 36 | 0.273438 | null | A text area (multi-line input) with syntax highlighting and autocomplete for Textual | [] | ['textual'] | 2024-01-12 | [] | 4 | 2 | null | 2.46 | 67 | 59 | 8 | 0 | 30 | 46 | 30 | 67 | 32 | 90 | 0.5 | 23 |
1,536 | study | https://github.com/gerdm/prml | [] | null | [] | [] | null | null | null | gerdm/prml | prml | 1,635 | 410 | 32 | Jupyter Notebook | null | Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop | gerdm | 2024-01-13 | 2018-11-23 | 270 | 6.042767 | null | Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop | ['bayesian-statistics', 'machine-learning', 'pattern-recognition', 'prml'] | ['bayesian-statistics', 'machine-learning', 'pattern-recognition', 'prml'] | 2022-07-25 | [('probml/pyprobml', 0.6533145308494568, 'ml', 1), ('brandon-rhodes/python-patterns', 0.5963156819343567, 'util', 0), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5888375639915466, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5584812760353088, 'study', 0), ('ageron/handson-ml2', 0.5364505648612976, 'ml', 0), ('mynameisfiber/high_performance_python_2e', 0.5304524302482605, 'study', 0), ('rasbt/mlxtend', 0.5303501486778259, 'ml', 1), ('wesm/pydata-book', 0.5239573121070862, 'study', 0), ('pymc-devs/pymc3', 0.5029430985450745, 'ml', 0), ('scikit-learn/scikit-learn', 0.5011841058731079, 'ml', 1)] | 3 | 1 | null | 0 | 0 | 0 | 63 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 22 |
1,012 | finance | https://github.com/quandl/quandl-python | [] | null | [] | [] | null | null | null | quandl/quandl-python | quandl-python | 1,343 | 391 | 131 | Python | null | null | quandl | 2024-01-09 | 2013-03-24 | 566 | 2.371594 | https://avatars.githubusercontent.com/u/1659378?v=4 | quandl/quandl-python | ['api-client', 'data-frame', 'dataset', 'quandl', 'retrieve-data'] | ['api-client', 'data-frame', 'dataset', 'quandl', 'retrieve-data'] | 2021-12-08 | [('cuemacro/findatapy', 0.5631470084190369, 'finance', 1)] | 36 | 2 | null | 0 | 0 | 0 | 132 | 26 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 22 |
593 | data | https://github.com/uber/fiber | [] | null | [] | [] | null | null | null | uber/fiber | fiber | 1,041 | 117 | 21 | Python | https://uber.github.io/fiber/ | Distributed Computing for AI Made Simple | uber | 2024-01-14 | 2020-01-07 | 212 | 4.910377 | https://avatars.githubusercontent.com/u/538264?v=4 | Distributed Computing for AI Made Simple | ['distributed-computing', 'machine-learning', 'multiprocessing', 'sandbox'] | ['distributed-computing', 'machine-learning', 'multiprocessing', 'sandbox'] | 2021-03-15 | [('paddlepaddle/paddle', 0.69061678647995, 'ml-dl', 1), ('horovod/horovod', 0.6222132444381714, 'ml-ops', 1), ('alpa-projects/alpa', 0.5923050045967102, 'ml-dl', 2), ('ray-project/ray', 0.5900027751922607, 'ml-ops', 1), ('tensorflow/tensorflow', 0.5713419914245605, 'ml-dl', 1), ('hpcaitech/colossalai', 0.5713132619857788, 'llm', 1), ('determined-ai/determined', 0.5588817596435547, 'ml-ops', 1), ('jina-ai/jina', 0.5508025884628296, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5499705672264099, 'ml', 1), ('bentoml/bentoml', 0.5481660962104797, 'ml-ops', 1), ('eventual-inc/daft', 0.5396055579185486, 'pandas', 2), ('mlflow/mlflow', 0.5344669818878174, 'ml-ops', 1), ('optuna/optuna', 0.5304052233695984, 'ml', 1), ('mlc-ai/mlc-llm', 0.522170901298523, 'llm', 0), ('fugue-project/fugue', 0.5197455883026123, 'pandas', 2), ('googlecloudplatform/vertex-ai-samples', 0.5184296369552612, 'ml', 0), ('nevronai/metisfl', 0.5169682502746582, 'ml', 1), ('pytorchlightning/pytorch-lightning', 0.512328028678894, 'ml-dl', 1), ('operand/agency', 0.5117772817611694, 'llm', 1), ('polyaxon/polyaxon', 0.5087381601333618, 'ml-ops', 1), ('backtick-se/cowait', 0.5072715282440186, 'util', 0), ('adap/flower', 0.505595326423645, 'ml-ops', 1), ('ml-tooling/opyrator', 0.5049328804016113, 'viz', 1), ('skypilot-org/skypilot', 0.5006906390190125, 'llm', 1)] | 5 | 3 | null | 0 | 0 | 0 | 49 | 34 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 22 |
1,693 | util | https://github.com/getsentry/milksnake | ['setuptools'] | null | [] | [] | null | null | null | getsentry/milksnake | milksnake | 784 | 36 | 23 | Python | null | A setuptools/wheel/cffi extension to embed a binary data in wheels | getsentry | 2024-01-12 | 2017-10-03 | 330 | 2.375758 | https://avatars.githubusercontent.com/u/1396951?v=4 | A setuptools/wheel/cffi extension to embed a binary data in wheels | ['tag-production'] | ['setuptools', 'tag-production'] | 2023-04-11 | [('pypa/installer', 0.5740995407104492, 'util', 0)] | 8 | 3 | null | 0.02 | 0 | 0 | 76 | 9 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 22 |
299 | nlp | https://github.com/openai/grade-school-math | ['word-problem', 'math', 'dataset'] | GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems | [] | [] | null | null | null | openai/grade-school-math | grade-school-math | 744 | 121 | 11 | Python | null | null | openai | 2024-01-14 | 2021-10-20 | 118 | 6.259615 | https://avatars.githubusercontent.com/u/14957082?v=4 | GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems | [] | ['dataset', 'math', 'word-problem'] | 2021-11-19 | [] | 2 | 1 | null | 0 | 10 | 2 | 27 | 26 | 0 | 0 | 0 | 10 | 2 | 90 | 0.2 | 22 |
258 | crypto | https://github.com/pmaji/crypto-whale-watching-app | [] | null | [] | [] | null | null | null | pmaji/crypto-whale-watching-app | crypto-whale-watching-app | 590 | 142 | 47 | Python | null | Python Dash app that tracks whale activity in cryptocurrency markets. | pmaji | 2024-01-12 | 2018-01-23 | 314 | 1.878981 | null | Python Dash app that tracks whale activity in cryptocurrency markets. | ['bitcoin', 'bitcoin-api', 'bitcoin-price', 'cryptocurrency', 'cryptocurrency-exchanges', 'cryptocurrency-price-ticker', 'cryptocurrency-prices', 'dash', 'ethereum', 'ethereum-blockchain', 'ethereum-price', 'gdax', 'gdax-api', 'gdax-python', 'litecoin', 'litecoin-price', 'plotly', 'plotly-dash'] | ['bitcoin', 'bitcoin-api', 'bitcoin-price', 'cryptocurrency', 'cryptocurrency-exchanges', 'cryptocurrency-price-ticker', 'cryptocurrency-prices', 'dash', 'ethereum', 'ethereum-blockchain', 'ethereum-price', 'gdax', 'gdax-api', 'gdax-python', 'litecoin', 'litecoin-price', 'plotly', 'plotly-dash'] | 2023-08-09 | [('1200wd/bitcoinlib', 0.6108453869819641, 'crypto', 3), ('plotly/dash', 0.5946712493896484, 'viz', 3), ('gbeced/basana', 0.553300142288208, 'finance', 1), ('ethereum/web3.py', 0.5342921614646912, 'crypto', 0), ('ccxt/ccxt', 0.5305103063583374, 'crypto', 3), ('primal100/pybitcointools', 0.5292482972145081, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5264788269996643, 'finance', 0), ('bmoscon/cryptofeed', 0.5131617188453674, 'crypto', 3), ('holoviz/panel', 0.5126122236251831, 'viz', 1)] | 8 | 1 | null | 0.06 | 3 | 1 | 73 | 5 | 0 | 0 | 0 | 3 | 3 | 90 | 1 | 22 |
189 | math | https://github.com/dit/dit | [] | null | [] | [] | null | null | null | dit/dit | dit | 474 | 83 | 25 | Python | http://docs.dit.io | Python package for information theory. | dit | 2024-01-09 | 2013-09-29 | 539 | 0.87894 | https://avatars.githubusercontent.com/u/3247210?v=4 | Python package for information theory. | ['information-theory'] | ['information-theory'] | 2023-08-30 | [('goldsmith/wikipedia', 0.525985062122345, 'data', 0), ('pytoolz/toolz', 0.5121693015098572, 'util', 0), ('ta-lib/ta-lib-python', 0.5039918422698975, 'finance', 0), ('quantecon/quantecon.py', 0.5037375688552856, 'sim', 0)] | 21 | 3 | null | 0.17 | 2 | 1 | 125 | 5 | 0 | 3 | 3 | 2 | 0 | 90 | 0 | 22 |
1,555 | util | https://github.com/kellyjonbrazil/jello | ['jq'] | null | [] | [] | null | null | null | kellyjonbrazil/jello | jello | 442 | 19 | 11 | Python | null | CLI tool to filter JSON and JSON Lines data with Python syntax. (Similar to jq) | kellyjonbrazil | 2024-01-14 | 2020-03-22 | 201 | 2.195884 | null | CLI tool to filter JSON and JSON Lines data with Python syntax. (Similar to jq) | ['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'filter', 'jq', 'json', 'json-lines', 'process', 'query', 'scripting', 'shell-scripting'] | ['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'filter', 'jq', 'json', 'json-lines', 'process', 'query', 'scripting', 'shell-scripting'] | 2023-04-29 | [('kellyjonbrazil/jc', 0.7774760127067566, 'util', 10), ('kellyjonbrazil/jellex', 0.7509535551071167, 'term', 5), ('brokenloop/jsontopydantic', 0.5349708795547485, 'util', 0), ('google/python-fire', 0.5335484743118286, 'term', 1), ('scikit-hep/awkward-1.0', 0.5297543406486511, 'data', 1), ('tiangolo/sqlmodel', 0.5081574320793152, 'data', 1)] | 2 | 1 | null | 0.33 | 1 | 1 | 46 | 9 | 2 | 10 | 2 | 1 | 1 | 90 | 1 | 22 |
573 | jupyter | https://github.com/computationalmodelling/nbval | [] | null | [] | [] | null | null | null | computationalmodelling/nbval | nbval | 425 | 51 | 11 | Python | null | A py.test plugin to validate Jupyter notebooks | computationalmodelling | 2024-01-05 | 2015-04-09 | 459 | 0.924487 | https://avatars.githubusercontent.com/u/11869420?v=4 | A py.test plugin to validate Jupyter notebooks | ['ipython-notebook', 'jupyter-notebook', 'pytest', 'pytest-plugin', 'testing'] | ['ipython-notebook', 'jupyter-notebook', 'pytest', 'pytest-plugin', 'testing'] | 2023-01-11 | [('nteract/testbook', 0.764438271522522, 'jupyter', 2), ('jupyter/notebook', 0.671747624874115, 'jupyter', 1), ('teemu/pytest-sugar', 0.6413612365722656, 'testing', 3), ('ipython/ipykernel', 0.6354666948318481, 'util', 1), ('jupyter-widgets/ipywidgets', 0.6210037469863892, 'jupyter', 0), ('ionelmc/pytest-benchmark', 0.6038401126861572, 'testing', 1), ('pytest-dev/pytest-xdist', 0.5855597257614136, 'testing', 2), ('pytest-dev/pytest-asyncio', 0.5843405723571777, 'testing', 2), ('jupyter/nbformat', 0.5732461214065552, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.5705468654632568, 'jupyter', 1), ('pytest-dev/pytest-cov', 0.5596536993980408, 'testing', 1), ('pytest-dev/pytest-mock', 0.5595971345901489, 'testing', 1), ('mwouts/jupytext', 0.5556824803352356, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.5462405681610107, 'jupyter', 0), ('wolever/parameterized', 0.5427061319351196, 'testing', 0), ('ipython/ipyparallel', 0.5368309617042542, 'perf', 0), ('kiwicom/pytest-recording', 0.535578191280365, 'testing', 2), ('samuelcolvin/dirty-equals', 0.5271663665771484, 'util', 1), ('jupyterlite/jupyterlite', 0.5270730257034302, 'jupyter', 0), ('samuelcolvin/pytest-pretty', 0.5243958234786987, 'testing', 1), ('pypa/virtualenv', 0.523438036441803, 'util', 0), ('taverntesting/tavern', 0.5216328501701355, 'testing', 2), ('voila-dashboards/voila', 0.5194684267044067, 'jupyter', 1), ('jupyter/nbconvert', 0.5174958109855652, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5154886841773987, 'jupyter', 1), ('pytest-dev/pytest', 0.5131306648254395, 'testing', 1), ('nbqa-dev/nbqa', 0.5108411312103271, 'jupyter', 1), ('jupyter/nbdime', 0.5095821022987366, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5067647099494934, 'study', 0)] | 33 | 3 | null | 0 | 2 | 0 | 107 | 12 | 0 | 2 | 2 | 2 | 1 | 90 | 0.5 | 22 |
1,191 | llm | https://github.com/reasoning-machines/pal | [] | null | [] | [] | null | null | null | reasoning-machines/pal | pal | 405 | 46 | 7 | Python | https://reasonwithpal.com | PaL: Program-Aided Language Models (ICML 2023) | reasoning-machines | 2024-01-11 | 2022-11-18 | 62 | 6.472603 | https://avatars.githubusercontent.com/u/118758190?v=4 | PaL: Program-Aided Language Models (ICML 2023) | ['commonsense-reasoning', 'few-shot-learning', 'language-generation', 'language-model', 'large-language-models', 'reasoning'] | ['commonsense-reasoning', 'few-shot-learning', 'language-generation', 'language-model', 'large-language-models', 'reasoning'] | 2023-06-30 | [('lupantech/chameleon-llm', 0.672268271446228, 'llm', 2), ('freedomintelligence/llmzoo', 0.6142875552177429, 'llm', 1), ('jonasgeiping/cramming', 0.6135402917861938, 'nlp', 1), ('juncongmoo/pyllama', 0.6026532053947449, 'llm', 0), ('hannibal046/awesome-llm', 0.5971046090126038, 'study', 1), ('ofa-sys/ofa', 0.5922104120254517, 'llm', 0), ('stanfordnlp/dspy', 0.5881893038749695, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5877846479415894, 'llm', 1), ('srush/minichain', 0.5743135809898376, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5670881867408752, 'llm', 1), ('guidance-ai/guidance', 0.5653203129768372, 'llm', 1), ('lianjiatech/belle', 0.5578760504722595, 'llm', 0), ('microsoft/autogen', 0.5551219582557678, 'llm', 0), ('openlmlab/moss', 0.5542696714401245, 'llm', 2), ('lm-sys/fastchat', 0.54938805103302, 'llm', 1), ('infinitylogesh/mutate', 0.5475483536720276, 'nlp', 1), ('ai21labs/lm-evaluation', 0.5380016565322876, 'llm', 1), ('kyegomez/tree-of-thoughts', 0.5376133918762207, 'llm', 0), ('nvlabs/prismer', 0.5364494323730469, 'diffusion', 1), ('next-gpt/next-gpt', 0.532139778137207, 'llm', 1), ('yueyu1030/attrprompt', 0.5289052128791809, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5286970734596252, 'llm', 1), ('neulab/prompt2model', 0.5284048318862915, 'llm', 1), ('databrickslabs/dolly', 0.5263434052467346, 'llm', 0), ('llmware-ai/llmware', 0.5255475640296936, 'llm', 1), ('cg123/mergekit', 0.5245123505592346, 'llm', 0), ('young-geng/easylm', 0.5207021832466125, 'llm', 2), ('conceptofmind/toolformer', 0.5196791887283325, 'llm', 1), ('huggingface/text-generation-inference', 0.5183734893798828, 'llm', 0), ('salesforce/blip', 0.512751042842865, 'diffusion', 0), ('paddlepaddle/paddlenlp', 0.5125070214271545, 'llm', 0), ('eth-sri/lmql', 0.5124557018280029, 'llm', 1), ('optimalscale/lmflow', 0.5115619897842407, 'llm', 1), ('likenneth/honest_llama', 0.5100179314613342, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5085762739181519, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5067288279533386, 'llm', 1), ('aiwaves-cn/agents', 0.5023298263549805, 'nlp', 1), ('explosion/spacy-models', 0.5003161430358887, 'nlp', 0), ('facebookresearch/shepherd', 0.5001876950263977, 'llm', 1)] | 4 | 1 | null | 0.08 | 0 | 0 | 14 | 7 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 22 |
1,044 | data | https://github.com/ydataai/ydata-quality | [] | null | [] | [] | null | null | null | ydataai/ydata-quality | ydata-quality | 396 | 52 | 8 | Jupyter Notebook | null | Data Quality assessment with one line of code | ydataai | 2024-01-13 | 2021-06-24 | 135 | 2.917895 | https://avatars.githubusercontent.com/u/57689451?v=4 | Data Quality assessment with one line of code | ['data', 'machine-learning', 'pandas', 'quality-assessment'] | ['data', 'machine-learning', 'pandas', 'quality-assessment'] | 2023-04-05 | [('ydataai/ydata-profiling', 0.6654260754585266, 'pandas', 2), ('cleanlab/cleanlab', 0.583878219127655, 'ml', 0), ('koaning/doubtlab', 0.5491899251937866, 'data', 0), ('great-expectations/great_expectations', 0.5358034372329712, 'ml-ops', 0), ('unionai-oss/pandera', 0.5350058078765869, 'pandas', 1), ('rubik/radon', 0.5341893434524536, 'util', 0)] | 10 | 2 | null | 0.02 | 12 | 0 | 31 | 9 | 1 | 5 | 1 | 12 | 1 | 90 | 0.1 | 22 |
159 | jupyter | https://github.com/nteract/testbook | [] | null | [] | [] | null | null | null | nteract/testbook | testbook | 374 | 41 | 17 | Python | https://testbook.readthedocs.io | 🧪 📗 Unit test your Jupyter Notebooks the right way | nteract | 2024-01-04 | 2020-02-26 | 204 | 1.825662 | https://avatars.githubusercontent.com/u/12401040?v=4 | 🧪 📗 Unit test your Jupyter Notebooks the right way | ['jupyter-notebook', 'nteract', 'pytest', 'testbook', 'unit-testing'] | ['jupyter-notebook', 'nteract', 'pytest', 'testbook', 'unit-testing'] | 2022-11-29 | [('computationalmodelling/nbval', 0.764438271522522, 'jupyter', 2), ('jupyter/notebook', 0.6220455765724182, 'jupyter', 1), ('jupyter/nbformat', 0.5974855422973633, 'jupyter', 0), ('samuelcolvin/dirty-equals', 0.5593006014823914, 'util', 2), ('jupyter/nbconvert', 0.5553418397903442, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.5517867207527161, 'jupyter', 0), ('ionelmc/pytest-benchmark', 0.5495516061782837, 'testing', 1), ('ipython/ipykernel', 0.5405094623565674, 'util', 1), ('pytest-dev/pytest-mock', 0.5319708585739136, 'testing', 1), ('jupyterlab/jupyterlab-desktop', 0.5236980319023132, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.5169227719306946, 'jupyter', 0)] | 15 | 5 | null | 0 | 0 | 0 | 47 | 14 | 0 | 4 | 4 | 0 | 0 | 90 | 0 | 22 |
738 | perf | https://github.com/blosc/python-blosc | [] | null | [] | [] | null | null | null | blosc/python-blosc | python-blosc | 337 | 117 | 15 | Python | https://www.blosc.org/python-blosc/python-blosc.html | A Python wrapper for the extremely fast Blosc compression library | blosc | 2024-01-12 | 2010-09-30 | 695 | 0.484394 | https://avatars.githubusercontent.com/u/6469856?v=4 | A Python wrapper for the extremely fast Blosc compression library | ['blosc', 'compression', 'wrapper'] | ['blosc', 'compression', 'wrapper'] | 2023-05-01 | [('zarr-developers/zarr-python', 0.5569503903388977, 'data', 0), ('ultrajson/ultrajson', 0.5216162204742432, 'perf', 0)] | 44 | 8 | null | 0.06 | 1 | 0 | 162 | 9 | 0 | 4 | 4 | 1 | 0 | 90 | 0 | 22 |
276 | data | https://github.com/airbnb/omniduct | [] | null | [] | [] | null | null | null | airbnb/omniduct | omniduct | 248 | 53 | 29 | Python | null | A toolkit providing a uniform interface for connecting to and extracting data from a wide variety of (potentially remote) data stores (including HDFS, Hive, Presto, MySQL, etc). | airbnb | 2023-11-14 | 2017-02-22 | 361 | 0.685353 | https://avatars.githubusercontent.com/u/698437?v=4 | A toolkit providing a uniform interface for connecting to and extracting data from a wide variety of (potentially remote) data stores (including HDFS, Hive, Presto, MySQL, etc). | [] | [] | 2023-11-01 | [('airbytehq/airbyte', 0.6164752244949341, 'data', 0), ('simonw/datasette', 0.6034160256385803, 'data', 0), ('aws/aws-sdk-pandas', 0.5810672044754028, 'pandas', 0), ('intake/intake', 0.567048192024231, 'data', 0), ('meltano/meltano', 0.5620505213737488, 'ml-ops', 0), ('databricks/dbt-databricks', 0.5590947866439819, 'data', 0), ('saulpw/visidata', 0.5526487231254578, 'term', 0), ('nasdaq/data-link-python', 0.5350242853164673, 'finance', 0), ('pytables/pytables', 0.5307878851890564, 'data', 0), ('duckdb/dbt-duckdb', 0.5301816463470459, 'data', 0), ('fugue-project/fugue', 0.5275896787643433, 'pandas', 0), ('pynamodb/pynamodb', 0.5225140452384949, 'data', 0), ('hyperqueryhq/whale', 0.5135779976844788, 'data', 0), ('apache/spark', 0.5120821595191956, 'data', 0), ('databrickslabs/dbx', 0.5089248418807983, 'data', 0)] | 12 | 5 | null | 0.04 | 9 | 1 | 84 | 2 | 1 | 11 | 1 | 9 | 2 | 90 | 0.2 | 22 |
1,228 | util | https://github.com/jaraco/wolframalpha | [] | null | [] | [] | null | null | null | jaraco/wolframalpha | wolframalpha | 138 | 26 | 8 | Python | null | null | jaraco | 2024-01-12 | 2015-11-25 | 426 | 0.323293 | null | jaraco/wolframalpha | [] | [] | 2023-12-26 | [] | 22 | 6 | null | 0.9 | 0 | 0 | 99 | 1 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 22 |
1,857 | llm | https://github.com/fasteval/fasteval | ['evaluation', 'benchmarks'] | null | [] | [] | null | null | null | fasteval/fasteval | FastEval | 129 | 16 | 3 | Python | https://fasteval.github.io/FastEval/ | Fast & more realistic evaluation of chat language models. Includes leaderboard. | fasteval | 2024-01-13 | 2023-05-07 | 38 | 3.369403 | https://avatars.githubusercontent.com/u/141508208?v=4 | Fast & more realistic evaluation of chat language models. Includes leaderboard. | ['benchmark', 'evaluation', 'llm'] | ['benchmark', 'benchmarks', 'evaluation', 'llm'] | 2023-11-14 | [('lm-sys/fastchat', 0.7203408479690552, 'llm', 1), ('nomic-ai/gpt4all', 0.6797701120376587, 'llm', 0), ('freedomintelligence/llmzoo', 0.6659313440322876, 'llm', 0), ('thudm/chatglm2-6b', 0.6485232710838318, 'llm', 1), ('rcgai/simplyretrieve', 0.6337692141532898, 'llm', 0), ('microsoft/autogen', 0.6213794350624084, 'llm', 0), ('embedchain/embedchain', 0.6101776957511902, 'llm', 1), ('run-llama/rags', 0.6045003533363342, 'llm', 1), ('young-geng/easylm', 0.6013676524162292, 'llm', 0), ('blinkdl/chatrwkv', 0.5970721244812012, 'llm', 0), ('ai21labs/lm-evaluation', 0.5941077470779419, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5938782095909119, 'perf', 0), ('openlmlab/moss', 0.593492329120636, 'llm', 0), ('mlc-ai/web-llm', 0.5912193655967712, 'llm', 1), ('next-gpt/next-gpt', 0.57789546251297, 'llm', 1), ('databrickslabs/dolly', 0.5695880055427551, 'llm', 0), ('rasahq/rasa', 0.5695826411247253, 'llm', 0), ('li-plus/chatglm.cpp', 0.5682762265205383, 'llm', 0), ('chatarena/chatarena', 0.5657923221588135, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5639832019805908, 'nlp', 0), ('hwchase17/langchain', 0.5635910630226135, 'llm', 0), ('minimaxir/simpleaichat', 0.559170663356781, 'llm', 0), ('deepset-ai/haystack', 0.5561326742172241, 'llm', 0), ('confident-ai/deepeval', 0.5521769523620605, 'testing', 2), ('nvidia/nemo', 0.5468846559524536, 'nlp', 0), ('deeppavlov/deeppavlov', 0.5457442402839661, 'nlp', 0), ('cheshire-cat-ai/core', 0.5404486060142517, 'llm', 1), ('openlmlab/leval', 0.5392605662345886, 'llm', 1), ('hiyouga/llama-factory', 0.5391845107078552, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5391843914985657, 'llm', 1), ('guidance-ai/guidance', 0.5377461910247803, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5358782410621643, 'llm', 2), ('langchain-ai/chat-langchain', 0.534351110458374, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5321249961853027, 'llm', 0), ('killianlucas/open-interpreter', 0.5310702919960022, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5244007110595703, 'llm', 0), ('dylanhogg/llmgraph', 0.5239781141281128, 'ml', 1), ('microsoft/lora', 0.5221768021583557, 'llm', 0), ('togethercomputer/openchatkit', 0.519807755947113, 'nlp', 0), ('hannibal046/awesome-llm', 0.5191826820373535, 'study', 0), ('aiwaves-cn/agents', 0.5190715789794922, 'nlp', 1), ('xtekky/gpt4free', 0.5166919827461243, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.514625608921051, 'llm', 0), ('juncongmoo/pyllama', 0.5125579833984375, 'llm', 0), ('paddlepaddle/paddlenlp', 0.507324755191803, 'llm', 1), ('facebookresearch/parlai', 0.5017014145851135, 'nlp', 0), ('lchen001/llmdrift', 0.5002023577690125, 'llm', 0)] | 2 | 0 | null | 19.27 | 6 | 2 | 8 | 2 | 0 | 0 | 0 | 6 | 3 | 90 | 0.5 | 22 |
554 | gis | https://github.com/darribas/gds_env | [] | null | [] | [] | null | null | null | darribas/gds_env | gds_env | 118 | 41 | 12 | Jupyter Notebook | https://darribas.org/gds_env | A containerised platform for Geographic Data Science | darribas | 2023-12-16 | 2016-08-12 | 389 | 0.302897 | null | A containerised platform for Geographic Data Science | ['docker', 'geographic-data-science', 'jupyter-lab', 'latex', 'r'] | ['docker', 'geographic-data-science', 'jupyter-lab', 'latex', 'r'] | 2023-10-24 | [('backtick-se/cowait', 0.6343125104904175, 'util', 1), ('orchest/orchest', 0.5977623462677002, 'ml-ops', 1), ('opengeos/leafmap', 0.563289999961853, 'gis', 0), ('airbytehq/airbyte', 0.5391685962677002, 'data', 0), ('mamba-org/micromamba-docker', 0.5356658697128296, 'util', 1), ('eventual-inc/daft', 0.5175384879112244, 'pandas', 0), ('domlysz/blendergis', 0.5147411823272705, 'gis', 0), ('multi-py/python-gunicorn', 0.5109146237373352, 'util', 1), ('simonw/datasette', 0.5070315599441528, 'data', 1), ('raphaelquast/eomaps', 0.5032002329826355, 'gis', 0), ('osgeo/grass', 0.502344012260437, 'gis', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5019727349281311, 'web', 1)] | 9 | 5 | null | 1.19 | 3 | 1 | 90 | 3 | 3 | 2 | 3 | 3 | 1 | 90 | 0.3 | 22 |
1,498 | llm | https://github.com/titanml/takeoff | ['inference', 'language-model'] | A server designed for optimized inference of large language models | [] | [] | null | null | null | titanml/takeoff | takeoff-community | 107 | 12 | 7 | HTML | https://docs.titanml.co/ | TitanML Takeoff Server is an optimization, compression and deployment platform that makes state of the art machine learning models accessible to everyone. | titanml | 2024-01-09 | 2023-07-31 | 26 | 4.092896 | https://avatars.githubusercontent.com/u/135022454?v=4 | TitanML Takeoff Server is an optimization, compression and deployment platform that makes state of the art machine learning models accessible to everyone. | ['deployment', 'llama', 'llm', 'quantization'] | ['deployment', 'inference', 'language-model', 'llama', 'llm', 'quantization'] | 2023-11-17 | [('ml-tooling/opyrator', 0.6359823942184448, 'viz', 1), ('bigscience-workshop/petals', 0.6349102854728699, 'data', 1), ('vllm-project/vllm', 0.5988606214523315, 'llm', 3), ('young-geng/easylm', 0.5864971876144409, 'llm', 2), ('unionai-oss/unionml', 0.56184321641922, 'ml-ops', 0), ('aws/sagemaker-python-sdk', 0.5581379532814026, 'ml', 0), ('huggingface/transformers', 0.5578950047492981, 'nlp', 1), ('bentoml/openllm', 0.5552429556846619, 'ml-ops', 2), ('microsoft/nni', 0.5543978214263916, 'ml', 0), ('bobazooba/xllm', 0.551180899143219, 'llm', 2), ('aiqc/aiqc', 0.550189733505249, 'ml-ops', 0), ('paddlepaddle/paddlenlp', 0.5484669208526611, 'llm', 2), ('mlc-ai/web-llm', 0.548348069190979, 'llm', 2), ('huggingface/datasets', 0.5470577478408813, 'nlp', 0), ('huawei-noah/pretrained-language-model', 0.5469074249267578, 'nlp', 1), ('mlflow/mlflow', 0.5464853048324585, 'ml-ops', 0), ('determined-ai/determined', 0.5414642691612244, 'ml-ops', 0), ('mlc-ai/mlc-llm', 0.5372212529182434, 'llm', 2), ('lm-sys/fastchat', 0.536109447479248, 'llm', 1), ('ludwig-ai/ludwig', 0.5331388711929321, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5318194031715393, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.531377375125885, 'ml', 0), ('alpa-projects/alpa', 0.5302854776382446, 'ml-dl', 1), ('horovod/horovod', 0.529777467250824, 'ml-ops', 0), ('nebuly-ai/nebullvm', 0.5286065936088562, 'perf', 1), ('ray-project/ray', 0.5229300856590271, 'ml-ops', 1), ('squeezeailab/squeezellm', 0.5222111940383911, 'llm', 3), ('kubeflow/fairing', 0.5214179754257202, 'ml-ops', 0), ('ray-project/ray-llm', 0.5190406441688538, 'llm', 1), ('bentoml/bentoml', 0.5187811851501465, 'ml-ops', 0), ('mlc-ai/web-stable-diffusion', 0.5177373886108398, 'diffusion', 0), ('lianjiatech/belle', 0.517549991607666, 'llm', 1), ('tairov/llama2.mojo', 0.5167184472084045, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5163436532020569, 'perf', 0), ('jzhang38/tinyllama', 0.5162729620933533, 'llm', 2), ('deepmind/dm-haiku', 0.5155620574951172, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5151104927062988, 'ml-dl', 0), ('pathwaycom/llm-app', 0.5129966735839844, 'llm', 1), ('zenml-io/zenml', 0.5115346908569336, 'ml-ops', 1), ('microsoft/jarvis', 0.5107346773147583, 'llm', 0), ('ravenscroftj/turbopilot', 0.5088446140289307, 'llm', 1), ('microsoft/onnxruntime', 0.5081208348274231, 'ml', 0), ('tatsu-lab/stanford_alpaca', 0.507792055606842, 'llm', 1), ('selfexplainml/piml-toolbox', 0.5073431134223938, 'ml-interpretability', 0), ('uber/petastorm', 0.5053594708442688, 'data', 0), ('salesforce/xgen', 0.5039275884628296, 'llm', 2), ('databrickslabs/dolly', 0.5038862228393555, 'llm', 0), ('predibase/lorax', 0.50351482629776, 'llm', 2), ('tigerlab-ai/tiger', 0.5030698180198669, 'llm', 1), ('microsoft/lmops', 0.5029642581939697, 'llm', 2), ('argilla-io/argilla', 0.5014408230781555, 'nlp', 1), ('tensorflow/tensorflow', 0.50138258934021, 'ml-dl', 0), ('optimalscale/lmflow', 0.5003492832183838, 'llm', 1)] | 5 | 2 | null | 0.62 | 3 | 3 | 6 | 2 | 0 | 0 | 0 | 3 | 0 | 90 | 0 | 22 |
1,425 | llm | https://github.com/pan-ml/panml | [] | null | [] | [] | null | null | null | pan-ml/panml | panml | 104 | 15 | 4 | Python | null | PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation. | pan-ml | 2024-01-04 | 2023-05-11 | 37 | 2.757576 | https://avatars.githubusercontent.com/u/133195194?v=4 | PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation. | ['artificial-intelligence', 'machine-learning', 'natural-language-processing', 'open-source', 'prompt-engineering'] | ['artificial-intelligence', 'machine-learning', 'natural-language-processing', 'open-source', 'prompt-engineering'] | 2023-07-08 | [('selfexplainml/piml-toolbox', 0.5762995481491089, 'ml-interpretability', 0), ('microsoft/nni', 0.532913088798523, 'ml', 1), ('ofa-sys/ofa', 0.5308709740638733, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5278686285018921, 'study', 1), ('microsoft/lmops', 0.5039084553718567, 'llm', 0), ('mlflow/mlflow', 0.5007346868515015, 'ml-ops', 1)] | 5 | 2 | null | 1.33 | 0 | 0 | 8 | 6 | 25 | 52 | 25 | 0 | 0 | 90 | 0 | 22 |
1,597 | study | https://github.com/anyscale/ray-summit-2023-training | [] | null | [] | [] | null | null | null | anyscale/ray-summit-2023-training | ray-summit-2023-training | 81 | 27 | 10 | Jupyter Notebook | https://raysummit.anyscale.com/trainings | null | anyscale | 2024-01-04 | 2023-08-22 | 23 | 3.521739 | https://avatars.githubusercontent.com/u/51251046?v=4 | anyscale/ray-summit-2023-training | ['anyscale', 'genai', 'llm', 'llms', 'ray'] | ['anyscale', 'genai', 'llm', 'llms', 'ray'] | 2023-10-02 | [] | 7 | 5 | null | 1.1 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 22 |
1,454 | util | https://github.com/conda-forge/feedstocks | ['feedstocks', 'conda'] | null | [] | [] | null | null | null | conda-forge/feedstocks | feedstocks | 58 | 41 | 4 | null | null | All conda-forge feedstocks, in one convenient place | conda-forge | 2023-12-17 | 2016-01-13 | 419 | 0.138142 | https://avatars.githubusercontent.com/u/11897326?v=4 | All conda-forge feedstocks, in one convenient place | [] | ['conda', 'feedstocks'] | 2024-01-14 | [('conda-forge/conda-smithy', 0.8073468208312988, 'util', 0), ('mamba-org/quetz', 0.6082790493965149, 'util', 1), ('conda/conda-pack', 0.5661166906356812, 'util', 1), ('conda/conda-build', 0.5539048314094543, 'util', 1), ('conda-forge/miniforge', 0.5309390425682068, 'util', 0), ('mamba-org/boa', 0.5122072696685791, 'util', 1), ('conda/constructor', 0.507718563079834, 'util', 1)] | 13 | 1 | null | 1,203.9 | 0 | 0 | 97 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 22 |
1,178 | gamedev | https://github.com/pygamelib/pygamelib | [] | null | [] | [] | null | null | null | pygamelib/pygamelib | pygamelib | 56 | 37 | 4 | Python | https://www.pygamelib.org | A (not so) small python library for console (as in terminal) game development. It is developed as a framework to help learn development and python in an entertaining way. | pygamelib | 2024-01-11 | 2019-03-15 | 254 | 0.219978 | https://avatars.githubusercontent.com/u/67972986?v=4 | A (not so) small python library for console (as in terminal) game development. It is developed as a framework to help learn development and python in an entertaining way. | ['game-development', 'gamedev', 'hacktoberfest2023', 'kids-coding', 'roguelike-library', 'terminal-based'] | ['game-development', 'gamedev', 'hacktoberfest2023', 'kids-coding', 'roguelike-library', 'terminal-based'] | 2023-10-21 | [('urwid/urwid', 0.700258731842041, 'term', 0), ('pygame/pygame', 0.6512402892112732, 'gamedev', 2), ('jquast/blessed', 0.6294499635696411, 'term', 0), ('lordmauve/pgzero', 0.6199098229408264, 'gamedev', 0), ('pyglet/pyglet', 0.5976749062538147, 'gamedev', 1), ('pythonarcade/arcade', 0.5959578156471252, 'gamedev', 0), ('xonsh/xonsh', 0.5624502301216125, 'util', 0), ('panda3d/panda3d', 0.5582475662231445, 'gamedev', 2), ('kitao/pyxel', 0.5561051964759827, 'gamedev', 2), ('python/cpython', 0.5532333850860596, 'util', 0), ('pytoolz/toolz', 0.5512012839317322, 'util', 0), ('willmcgugan/textual', 0.539828896522522, 'term', 0), ('eleutherai/pyfra', 0.527156412601471, 'ml', 0), ('pypy/pypy', 0.5232526659965515, 'util', 0), ('r0x0r/pywebview', 0.5211628079414368, 'gui', 0), ('1200wd/bitcoinlib', 0.5186594128608704, 'crypto', 0), ('hoffstadt/dearpygui', 0.5179816484451294, 'gui', 0), ('dylanhogg/awesome-python', 0.5159634947776794, 'study', 0), ('webpy/webpy', 0.5152013301849365, 'web', 0), ('pokepetter/ursina', 0.5143521428108215, 'gamedev', 1), ('evhub/coconut', 0.5140511989593506, 'util', 0), ('amaargiru/pyroad', 0.5083182454109192, 'study', 0), ('pyscript/pyscript-cli', 0.5070560574531555, 'web', 0), ('google/python-fire', 0.5067519545555115, 'term', 0), ('willmcgugan/rich', 0.5059705376625061, 'term', 0)] | 28 | 3 | null | 1.23 | 21 | 9 | 59 | 3 | 0 | 2 | 2 | 21 | 9 | 90 | 0.4 | 22 |
1,707 | util | https://github.com/stijnwoestenborghs/gradi-mojo | ['mojo'] | Implementation of a simple gradient descent problem in Python, Numpy, JAX, C++ (binding with Python) and Mojo. | [] | [] | null | null | null | stijnwoestenborghs/gradi-mojo | gradi-mojo | 29 | 2 | 4 | C++ | null | null | stijnwoestenborghs | 2024-01-04 | 2023-10-02 | 17 | 1.691667 | null | Implementation of a simple gradient descent problem in Python, Numpy, JAX, C++ (binding with Python) and Mojo. | [] | ['mojo'] | 2023-12-03 | [('msaelices/py2mojo', 0.5529176592826843, 'util', 1), ('google/jax', 0.5190989971160889, 'ml', 0), ('cma-es/pycma', 0.5154417753219604, 'math', 0)] | 4 | 2 | null | 1.13 | 2 | 1 | 3 | 1 | 0 | 0 | 0 | 2 | 5 | 90 | 2.5 | 22 |
1,042 | llm | https://github.com/openai/image-gpt | [] | Archived. Code and models from the paper "Generative Pretraining from Pixels" | [] | [] | null | null | null | openai/image-gpt | image-gpt | 1,978 | 383 | 82 | Python | null | null | openai | 2024-01-12 | 2020-05-07 | 194 | 10.158474 | https://avatars.githubusercontent.com/u/14957082?v=4 | Archived. Code and models from the paper "Generative Pretraining from Pixels" | [] | [] | 2020-12-04 | [('davidadsp/generative_deep_learning_2nd_edition', 0.6263450980186462, 'study', 0), ('ist-daslab/gptq', 0.6169224381446838, 'llm', 0), ('openai/finetune-transformer-lm', 0.6050621867179871, 'llm', 0), ('bigcode-project/starcoder', 0.5846189260482788, 'llm', 0), ('salesforce/codegen', 0.5758861303329468, 'nlp', 0), ('thudm/cogvideo', 0.558464765548706, 'ml', 0), ('pollinations/dance-diffusion', 0.5518842935562134, 'diffusion', 0), ('jerryyli/valhalla-nmt', 0.5397126078605652, 'ml-dl', 0), ('open-mmlab/mmediting', 0.5370244383811951, 'ml', 0), ('salesforce/blip', 0.5261111855506897, 'diffusion', 0), ('nv-tlabs/get3d', 0.5187845826148987, 'ml', 0), ('timothybrooks/instruct-pix2pix', 0.512509286403656, 'diffusion', 0), ('karpathy/mingpt', 0.5077084302902222, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5061761140823364, 'study', 0), ('borisdayma/dalle-mini', 0.5032017827033997, 'diffusion', 0)] | 3 | 0 | null | 0 | 0 | 0 | 45 | 38 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 21 |
329 | nlp | https://github.com/franck-dernoncourt/neuroner | [] | null | [] | [] | null | null | null | franck-dernoncourt/neuroner | NeuroNER | 1,674 | 486 | 81 | Python | http://neuroner.com | Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results. | franck-dernoncourt | 2024-01-13 | 2017-03-07 | 360 | 4.65 | null | Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results. | ['deep-learning', 'machine-learning', 'named-entity-recognition', 'neural-networks', 'nlp', 'tensorflow'] | ['deep-learning', 'machine-learning', 'named-entity-recognition', 'neural-networks', 'nlp', 'tensorflow'] | 2019-10-02 | [('flairnlp/flair', 0.7272414565086365, 'nlp', 3), ('allenai/allennlp', 0.5776329040527344, 'nlp', 2), ('deeppavlov/deeppavlov', 0.5672761797904968, 'nlp', 5), ('keras-team/keras-nlp', 0.5547518134117126, 'nlp', 4), ('huggingface/neuralcoref', 0.5318391919136047, 'nlp', 3), ('nvidia/deeplearningexamples', 0.5285832285881042, 'ml-dl', 3), ('microsoft/vert-papers', 0.5278816223144531, 'nlp', 2), ('explosion/spacy', 0.527627170085907, 'nlp', 5), ('huggingface/transformers', 0.5211098790168762, 'nlp', 4), ('rasahq/rasa', 0.5182371735572815, 'llm', 2), ('paddlepaddle/paddlenlp', 0.5083341598510742, 'llm', 1), ('nvidia/nemo', 0.5075932145118713, 'nlp', 2), ('thilinarajapakse/simpletransformers', 0.5060073733329773, 'nlp', 1), ('deepset-ai/farm', 0.5047802329063416, 'nlp', 2), ('explosion/spacy-llm', 0.5040908455848694, 'llm', 3), ('nltk/nltk', 0.5021610856056213, 'nlp', 2)] | 7 | 2 | null | 0 | 0 | 0 | 83 | 52 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 21 |
666 | perf | https://github.com/markshannon/faster-cpython | [] | null | [] | [] | null | null | null | markshannon/faster-cpython | faster-cpython | 933 | 20 | 84 | null | null | How to make CPython faster. | markshannon | 2024-01-10 | 2020-10-19 | 171 | 5.451586 | null | How to make CPython faster. | [] | [] | 2020-10-28 | [('faster-cpython/tools', 0.8188387751579285, 'perf', 0), ('faster-cpython/ideas', 0.6732155084609985, 'perf', 0), ('brandtbucher/specialist', 0.5744246244430542, 'perf', 0), ('p403n1x87/austin', 0.5521705150604248, 'profiling', 0), ('python/cpython', 0.549221932888031, 'util', 0), ('pypy/pypy', 0.5423987507820129, 'util', 0), ('ipython/ipyparallel', 0.541959285736084, 'perf', 0), ('lcompilers/lpython', 0.5289682149887085, 'util', 0), ('cython/cython', 0.5285276770591736, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5006858706474304, 'perf', 0)] | 4 | 2 | null | 0 | 1 | 1 | 39 | 39 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 21 |
1,152 | util | https://github.com/alex-sherman/unsync | [] | null | [] | [] | null | null | null | alex-sherman/unsync | unsync | 860 | 50 | 21 | Python | null | Unsynchronize asyncio | alex-sherman | 2024-01-13 | 2018-02-06 | 312 | 2.75641 | null | Unsynchronize asyncio | [] | [] | 2022-02-06 | [('magicstack/uvloop', 0.6438055634498596, 'util', 0), ('erdewit/nest_asyncio', 0.597553551197052, 'util', 0), ('aio-libs/aiohttp', 0.5807369351387024, 'web', 0), ('timofurrer/awesome-asyncio', 0.5594347715377808, 'study', 0), ('agronholm/anyio', 0.5539295673370361, 'perf', 0), ('tiangolo/asyncer', 0.5449034571647644, 'perf', 0), ('pytest-dev/pytest-asyncio', 0.5358930230140686, 'testing', 0), ('samuelcolvin/arq', 0.5158486366271973, 'data', 0), ('aio-libs/aiokafka', 0.5082889199256897, 'data', 0), ('samuelcolvin/aioaws', 0.507796049118042, 'data', 0), ('noxdafox/pebble', 0.5057518482208252, 'perf', 0)] | 11 | 3 | null | 0 | 0 | 0 | 72 | 24 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 21 |
168 | nlp | https://github.com/lexpredict/lexpredict-lexnlp | [] | null | [] | [] | null | null | null | lexpredict/lexpredict-lexnlp | lexpredict-lexnlp | 659 | 171 | 51 | Jupyter Notebook | null | LexNLP by LexPredict | lexpredict | 2024-01-06 | 2017-09-30 | 330 | 1.99438 | https://avatars.githubusercontent.com/u/8458599?v=4 | LexNLP by LexPredict | ['analytics', 'contracts', 'data', 'law', 'legal', 'legaltech', 'linguistics', 'ml', 'nlp'] | ['analytics', 'contracts', 'data', 'law', 'legal', 'legaltech', 'linguistics', 'ml', 'nlp'] | 2023-03-07 | [('nltk/nltk', 0.6562715172767639, 'nlp', 1), ('coastalcph/lex-glue', 0.6552823185920715, 'nlp', 3), ('iclrandd/blackstone', 0.582179069519043, 'nlp', 3), ('explosion/spacy-llm', 0.5600504875183105, 'llm', 1), ('explosion/spacy', 0.5584388971328735, 'nlp', 1), ('sloria/textblob', 0.5583809018135071, 'nlp', 1), ('cgpotts/cs224u', 0.5473800897598267, 'study', 1), ('explosion/spacy-models', 0.5394803285598755, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5384023189544678, 'llm', 1), ('norskregnesentral/skweak', 0.5267290472984314, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.525945782661438, 'study', 1), ('allenai/allennlp', 0.524100661277771, 'nlp', 1), ('infinitylogesh/mutate', 0.5115991234779358, 'nlp', 0)] | 9 | 1 | null | 0.04 | 0 | 0 | 77 | 10 | 9 | 2 | 9 | 0 | 0 | 90 | 0 | 21 |
1,734 | data | https://github.com/mcfunley/pugsql | ['orm', 'hugsql', 'sql'] | null | [] | [] | null | null | null | mcfunley/pugsql | pugsql | 652 | 20 | 10 | Python | https://pugsql.org | A HugSQL-inspired database library for Python | mcfunley | 2024-01-13 | 2019-05-19 | 245 | 2.658125 | null | A HugSQL-inspired database library for Python | [] | ['hugsql', 'orm', 'sql'] | 2022-05-27 | [('tiangolo/sqlmodel', 0.6844363808631897, 'data', 1), ('sqlalchemy/sqlalchemy', 0.6739656329154968, 'data', 1), ('ibis-project/ibis', 0.6264197826385498, 'data', 1), ('collerek/ormar', 0.6217651963233948, 'data', 1), ('coleifer/peewee', 0.6096048951148987, 'data', 0), ('andialbrecht/sqlparse', 0.5752284526824951, 'data', 0), ('tobymao/sqlglot', 0.5487765073776245, 'data', 1), ('aio-libs/aiomysql', 0.5428056716918945, 'data', 0), ('qdrant/fastembed', 0.5356664657592773, 'ml', 0), ('nasdaq/data-link-python', 0.5342159867286682, 'finance', 0), ('kayak/pypika', 0.5262505412101746, 'data', 1), ('aio-libs/aiopg', 0.5243105888366699, 'data', 0), ('sdispater/orator', 0.5235227346420288, 'data', 1), ('strawberry-graphql/strawberry', 0.5126366019248962, 'web', 0), ('agronholm/sqlacodegen', 0.5109015703201294, 'data', 0), ('pytoolz/toolz', 0.5106123685836792, 'util', 0), ('sqlalchemy/alembic', 0.5098458528518677, 'data', 1), ('sfu-db/connector-x', 0.5034618377685547, 'data', 1), ('jina-ai/vectordb', 0.5005654096603394, 'data', 0)] | 12 | 1 | null | 0 | 2 | 1 | 57 | 20 | 0 | 4 | 4 | 2 | 0 | 90 | 0 | 21 |
274 | crypto | https://github.com/ethtx/ethtx | [] | null | [] | [] | null | null | null | ethtx/ethtx | ethtx | 447 | 74 | 16 | Python | https://www.ethtx.info | Python package with core transaction decoding functions. | ethtx | 2024-01-12 | 2021-06-28 | 135 | 3.307611 | https://avatars.githubusercontent.com/u/70520035?v=4 | Python package with core transaction decoding functions. | [] | [] | 2023-05-17 | [('pytoolz/toolz', 0.5787340402603149, 'util', 0), ('ethtx/ethtx_ce', 0.5777018666267395, 'crypto', 0), ('pmorissette/ffn', 0.5535359978675842, 'finance', 0), ('indygreg/pyoxidizer', 0.5071747303009033, 'util', 0), ('pdm-project/pdm', 0.5046972632408142, 'util', 0), ('pyston/pyston', 0.5028896331787109, 'util', 0)] | 6 | 2 | null | 0.08 | 1 | 0 | 31 | 8 | 1 | 15 | 1 | 1 | 0 | 90 | 0 | 21 |
1,071 | llm | https://github.com/bigscience-workshop/t-zero | [] | null | [] | [] | null | null | null | bigscience-workshop/t-zero | t-zero | 436 | 51 | 24 | Python | null | Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization) | bigscience-workshop | 2024-01-10 | 2021-12-13 | 111 | 3.922879 | https://avatars.githubusercontent.com/u/82455566?v=4 | Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization) | [] | [] | 2022-07-29 | [('huggingface/setfit', 0.5152558088302612, 'nlp', 0)] | 6 | 4 | null | 0 | 0 | 0 | 25 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 21 |
833 | util | https://github.com/carlospuenteg/file-injector | [] | null | [] | [] | null | null | null | carlospuenteg/file-injector | File-Injector | 421 | 24 | 7 | Python | null | File Injector is a script that allows you to store any file in an image using steganography | carlospuenteg | 2024-01-12 | 2022-10-22 | 66 | 6.337634 | null | File Injector is a script that allows you to store any file in an image using steganography | ['extraction', 'file', 'file-injection', 'file-injector', 'files', 'image', 'image-manipulation', 'image-processing', 'injection', 'noise', 'numpy', 'photography', 'steganography', 'storage'] | ['extraction', 'file', 'file-injection', 'file-injector', 'files', 'image', 'image-manipulation', 'image-processing', 'injection', 'noise', 'numpy', 'photography', 'steganography', 'storage'] | 2022-11-18 | [] | 1 | 0 | null | 0 | 0 | 0 | 15 | 14 | 0 | 11 | 11 | 0 | 0 | 90 | 0 | 21 |
1,651 | nlp | https://github.com/babelscape/rebel | [] | null | [] | [] | null | null | null | babelscape/rebel | rebel | 382 | 51 | 4 | Python | null | REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021). | babelscape | 2024-01-11 | 2021-09-06 | 125 | 3.052511 | https://avatars.githubusercontent.com/u/90899893?v=4 | REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021). | ['entity-linking', 'natural-language-generation', 'natural-language-processing', 'nlp', 'relation-extraction'] | ['entity-linking', 'natural-language-generation', 'natural-language-processing', 'nlp', 'relation-extraction'] | 2023-11-09 | [('microsoft/vert-papers', 0.5618175864219666, 'nlp', 2), ('zjunlp/deepke', 0.5535876154899597, 'ml', 2)] | 4 | 0 | null | 0.1 | 2 | 1 | 29 | 2 | 0 | 0 | 0 | 2 | 2 | 90 | 1 | 21 |
924 | llm | https://github.com/lucidrains/medical-chatgpt | [] | null | [] | [] | null | null | null | lucidrains/medical-chatgpt | medical-chatgpt | 307 | 30 | 32 | Python | null | Implementation of ChatGPT, but tailored towards primary care medicine, with the reward being able to collect patient histories in a thorough and efficient manner and come up with a reasonable differential diagnosis | lucidrains | 2024-01-04 | 2022-12-10 | 59 | 5.165865 | null | Implementation of ChatGPT, but tailored towards primary care medicine, with the reward being able to collect patient histories in a thorough and efficient manner and come up with a reasonable differential diagnosis | ['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'medicine', 'transformers'] | ['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'medicine', 'transformers'] | 2023-10-14 | [('oneil512/insight', 0.6099081635475159, 'ml', 0), ('project-monai/monai', 0.5313103795051575, 'ml', 1), ('epfllm/meditron', 0.5020337104797363, 'llm', 0)] | 2 | 1 | null | 0.1 | 0 | 0 | 13 | 3 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 21 |
844 | web | https://github.com/conradbez/hstream | [] | null | [] | [] | null | null | null | conradbez/hstream | hstream | 274 | 14 | 7 | Python | null | Hyper Stream | conradbez | 2024-01-04 | 2022-11-03 | 64 | 4.233996 | null | Hyper Stream | [] | [] | 2023-11-25 | [] | 5 | 1 | null | 0.4 | 0 | 0 | 15 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 21 |
1,810 | llm | https://github.com/alphasecio/langchain-examples | [] | null | [] | [] | null | null | null | alphasecio/langchain-examples | langchain-examples | 257 | 68 | 4 | Python | null | A collection of apps powered by the LangChain LLM framework. | alphasecio | 2024-01-13 | 2023-05-19 | 36 | 7.027344 | null | A collection of apps powered by the LangChain LLM framework. | ['genai', 'jupyter-notebook', 'langchain', 'llm', 'notebook', 'openai', 'pinecone', 'streamlit', 'vectordb'] | ['genai', 'jupyter-notebook', 'langchain', 'llm', 'notebook', 'openai', 'pinecone', 'streamlit', 'vectordb'] | 2023-09-28 | [('langchain-ai/langgraph', 0.6478251814842224, 'llm', 1), ('pathwaycom/llm-app', 0.6350022554397583, 'llm', 1), ('shishirpatil/gorilla', 0.6303489804267883, 'llm', 1), ('microsoft/semantic-kernel', 0.6260648965835571, 'llm', 2), ('nat/openplayground', 0.6207661628723145, 'llm', 0), ('explosion/spacy-streamlit', 0.6193504929542542, 'nlp', 1), ('lancedb/lancedb', 0.601751446723938, 'data', 1), ('tigerlab-ai/tiger', 0.5991637706756592, 'llm', 1), ('activeloopai/deeplake', 0.5867215991020203, 'ml-ops', 2), ('streamlit/streamlit', 0.5835210084915161, 'viz', 1), ('gradio-app/gradio', 0.5749422907829285, 'viz', 0), ('willmcgugan/textual', 0.570219874382019, 'term', 0), ('kivy/kivy', 0.5670594573020935, 'util', 0), ('microsoft/promptflow', 0.5629417896270752, 'llm', 1), ('cohere-ai/notebooks', 0.5620157718658447, 'llm', 0), ('hegelai/prompttools', 0.5608381628990173, 'llm', 0), ('prefecthq/langchain-prefect', 0.5599607229232788, 'llm', 1), ('chainlit/chainlit', 0.5578954815864563, 'llm', 3), ('gkamradt/langchain-tutorials', 0.5529564023017883, 'study', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5519778728485107, 'template', 0), ('salesforce/xgen', 0.5513124465942383, 'llm', 1), ('run-llama/rags', 0.5506168007850647, 'llm', 3), ('hwchase17/langchain', 0.5499154925346375, 'llm', 1), ('openai/tiktoken', 0.5489485263824463, 'nlp', 0), ('fastai/fastcore', 0.5439481735229492, 'util', 0), ('mlc-ai/web-llm', 0.5424495339393616, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5416777729988098, 'llm', 1), ('bobazooba/xllm', 0.5409016609191895, 'llm', 2), ('berriai/litellm', 0.5404641032218933, 'llm', 3), ('bigscience-workshop/petals', 0.5384784936904907, 'data', 0), ('eugeneyan/open-llms', 0.5366406440734863, 'study', 1), ('hiyouga/llama-efficient-tuning', 0.5362140536308289, 'llm', 1), ('hiyouga/llama-factory', 0.5362140536308289, 'llm', 1), ('aws/graph-notebook', 0.5361274480819702, 'jupyter', 1), ('nebuly-ai/nebullvm', 0.5353783369064331, 'perf', 1), ('embedchain/embedchain', 0.532405436038971, 'llm', 1), ('lianjiatech/belle', 0.5297924876213074, 'llm', 0), ('zilliztech/gptcache', 0.5278271436691284, 'llm', 3), ('nvidia/tensorrt-llm', 0.5265845060348511, 'viz', 0), ('deepset-ai/haystack', 0.5227251648902893, 'llm', 0), ('flet-dev/flet', 0.5201708078384399, 'web', 0), ('google/gin-config', 0.5197805762290955, 'util', 0), ('langchain-ai/langsmith-sdk', 0.5165998935699463, 'llm', 0), ('young-geng/easylm', 0.5165916681289673, 'llm', 0), ('dylanhogg/awesome-python', 0.5161072611808777, 'study', 0), ('alpha-vllm/llama2-accessory', 0.5159311890602112, 'llm', 0), ('agenta-ai/agenta', 0.5152485370635986, 'llm', 2), ('tiangolo/fastapi', 0.5145138502120972, 'web', 0), ('run-llama/llama-hub', 0.5134257674217224, 'data', 1), ('plotly/dash', 0.5120916962623596, 'viz', 0), ('zenodo/zenodo', 0.511971652507782, 'util', 0), ('mito-ds/monorepo', 0.5109399557113647, 'jupyter', 0), ('microsoft/autogen', 0.5106598138809204, 'llm', 0), ('langchain-ai/langsmith-cookbook', 0.5074201822280884, 'llm', 0), ('koaning/calm-notebooks', 0.5073686242103577, 'study', 0), ('cheshire-cat-ai/core', 0.5036561489105225, 'llm', 1), ('vitalik/django-ninja', 0.5026804804801941, 'web', 0), ('facebookresearch/hydra', 0.5024363994598389, 'util', 0), ('holoviz/panel', 0.500730037689209, 'viz', 0), ('h2oai/h2o-llmstudio', 0.5006608366966248, 'llm', 1), ('qdrant/fastembed', 0.5004814267158508, 'ml', 2), ('huggingface/huggingface_hub', 0.5002479553222656, 'ml', 0), ('chroma-core/chroma', 0.5002412796020508, 'data', 1)] | 1 | 0 | null | 2.44 | 1 | 0 | 8 | 4 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 21 |
551 | ml | https://github.com/nicolas-chaulet/torch-points3d | [] | null | [] | [] | null | null | null | nicolas-chaulet/torch-points3d | torch-points3d | 176 | 40 | 1 | null | https://torch-points3d.readthedocs.io/en/latest/ | Pytorch framework for doing deep learning on point clouds. | nicolas-chaulet | 2024-01-04 | 2022-01-09 | 107 | 1.640479 | null | Pytorch framework for doing deep learning on point clouds. | [] | [] | 2021-12-10 | [('pytorch/ignite', 0.6167212128639221, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.6138631701469421, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5965690016746521, 'study', 0), ('nvidia/apex', 0.5915487408638, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.580845832824707, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5780234932899475, 'perf', 0), ('skorch-dev/skorch', 0.5687388777732849, 'ml-dl', 0), ('huggingface/accelerate', 0.5572007894515991, 'ml', 0), ('ashleve/lightning-hydra-template', 0.5534338355064392, 'util', 0), ('denys88/rl_games', 0.5515884757041931, 'ml-rl', 0), ('tensorflow/mesh', 0.5492365956306458, 'ml-dl', 0), ('karpathy/micrograd', 0.5409857034683228, 'study', 0), ('lucidrains/imagen-pytorch', 0.5233572125434875, 'ml-dl', 0), ('openai/point-e', 0.5189539790153503, 'util', 0), ('nvlabs/gcvit', 0.5170150995254517, 'diffusion', 0), ('rasbt/machine-learning-book', 0.5115610957145691, 'study', 0), ('ageron/handson-ml2', 0.5103498101234436, 'ml', 0), ('rentruewang/koila', 0.5067731738090515, 'ml', 0), ('xl0/lovely-tensors', 0.5040701031684875, 'ml-dl', 0), ('dmlc/dgl', 0.501939594745636, 'ml-dl', 0), ('hazyresearch/hgcn', 0.5006260871887207, 'ml', 0)] | 29 | 6 | null | 0 | 0 | 0 | 24 | 25 | 0 | 5 | 5 | 0 | 0 | 90 | 0 | 21 |
1,484 | util | https://github.com/allrod5/injectable | ['dependency-injection'] | null | [] | [] | null | null | null | allrod5/injectable | injectable | 101 | 8 | 5 | Python | https://injectable.readthedocs.io | Python Dependency Injection for Humans™ | allrod5 | 2024-01-09 | 2018-02-04 | 312 | 0.323422 | null | Python Dependency Injection for Humans™ | ['autowired', 'autowiring', 'circular-dependencies', 'dependency-injection', 'for-humans', 'injection', 'ioc', 'lazy-evaluation', 'micro-framework'] | ['autowired', 'autowiring', 'circular-dependencies', 'dependency-injection', 'for-humans', 'injection', 'ioc', 'lazy-evaluation', 'micro-framework'] | 2023-01-11 | [('python-injector/injector', 0.6809417605400085, 'util', 1), ('ets-labs/python-dependency-injector', 0.6628846526145935, 'util', 2), ('ivankorobkov/python-inject', 0.640688955783844, 'util', 1), ('pytoolz/toolz', 0.5491871237754822, 'util', 0), ('eleutherai/pyfra', 0.5288636088371277, 'ml', 0), ('pdm-project/pdm', 0.5183610320091248, 'util', 0), ('python-poetry/poetry', 0.5134292244911194, 'util', 0), ('artemyk/dynpy', 0.5095717906951904, 'sim', 0), ('hoffstadt/dearpygui', 0.508715033531189, 'gui', 0), ('micropython/micropython', 0.5082573294639587, 'util', 0), ('google/pyglove', 0.5078258514404297, 'util', 0), ('grahamdumpleton/wrapt', 0.5006911754608154, 'util', 0), ('reloadware/reloadium', 0.5002021193504333, 'profiling', 0)] | 3 | 3 | null | 0 | 1 | 0 | 72 | 12 | 0 | 5 | 5 | 1 | 3 | 90 | 3 | 21 |
965 | data | https://github.com/vmiklos/ged2dot | [] | null | [] | [] | null | null | null | vmiklos/ged2dot | ged2dot | 93 | 19 | 9 | Python | https://vmiklos.hu/ged2dot/ | GEDCOM to Graphviz converter | vmiklos | 2023-11-17 | 2013-11-01 | 534 | 0.173971 | null | GEDCOM to Graphviz converter | ['dot', 'gedcom', 'libreoffice'] | ['dot', 'gedcom', 'libreoffice'] | 2024-01-01 | [('pydot/pydot', 0.5854193568229675, 'viz', 0), ('pygraphviz/pygraphviz', 0.5002699494361877, 'viz', 0)] | 9 | 2 | null | 0.54 | 7 | 7 | 124 | 0 | 2 | 2 | 2 | 7 | 2 | 90 | 0.3 | 21 |
814 | pandas | https://github.com/ddelange/mapply | [] | null | [] | [] | null | null | null | ddelange/mapply | mapply | 63 | 3 | 5 | Python | null | Sensible multi-core apply function for Pandas | ddelange | 2024-01-08 | 2020-10-26 | 170 | 0.370277 | null | Sensible multi-core apply function for Pandas | [] | [] | 2024-01-13 | [('jmcarpenter2/swifter', 0.6554047465324402, 'pandas', 0), ('nalepae/pandarallel', 0.6456267833709717, 'pandas', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5479068756103516, 'pandas', 0), ('blaze/blaze', 0.513414204120636, 'pandas', 0)] | 2 | 0 | null | 0.4 | 20 | 20 | 39 | 0 | 6 | 8 | 6 | 20 | 38 | 90 | 1.9 | 21 |
391 | pandas | https://github.com/tkrabel/bamboolib | [] | null | [] | [] | null | null | null | tkrabel/bamboolib | bamboolib | 921 | 94 | 32 | Jupyter Notebook | https://bamboolib.com | bamboolib - a GUI for pandas DataFrames | tkrabel | 2024-01-04 | 2019-05-29 | 243 | 3.776801 | null | bamboolib - a GUI for pandas DataFrames | ['jupyter-notebook', 'jupyterlab', 'pandas', 'pandas-dataframes'] | ['jupyter-notebook', 'jupyterlab', 'pandas', 'pandas-dataframes'] | 2022-09-27 | [('adamerose/pandasgui', 0.8184458017349243, 'pandas', 1), ('quantopian/qgrid', 0.7011144161224365, 'jupyter', 0), ('lux-org/lux', 0.6735276579856873, 'viz', 1), ('jakevdp/pythondatasciencehandbook', 0.6585032939910889, 'study', 2), ('bloomberg/ipydatagrid', 0.6406834721565247, 'jupyter', 0), ('kanaries/pygwalker', 0.6401150822639465, 'pandas', 1), ('holoviz/panel', 0.620628833770752, 'viz', 0), ('cmudig/autoprofiler', 0.6205199956893921, 'jupyter', 1), ('man-group/dtale', 0.6187593936920166, 'viz', 2), ('jupyter-widgets/ipywidgets', 0.5929217338562012, 'jupyter', 0), ('twopirllc/pandas-ta', 0.5758503675460815, 'finance', 2), ('vizzuhq/ipyvizzu', 0.5739924311637878, 'jupyter', 1), ('beeware/toga', 0.5691761374473572, 'gui', 0), ('mwaskom/seaborn', 0.5646217465400696, 'viz', 1), ('geopandas/geopandas', 0.5592805743217468, 'gis', 1), ('wesm/pydata-book', 0.557572066783905, 'study', 0), ('jupyterlab/jupyterlab-desktop', 0.5493032336235046, 'jupyter', 2), ('jazzband/tablib', 0.5476372241973877, 'data', 0), ('pandas-dev/pandas', 0.5454512238502502, 'pandas', 1), ('jmcarpenter2/swifter', 0.5419985055923462, 'pandas', 1), ('delta-io/delta-rs', 0.5383087396621704, 'pandas', 1), ('aws/graph-notebook', 0.5372664928436279, 'jupyter', 1), ('eleutherai/pyfra', 0.53708815574646, 'ml', 0), ('voila-dashboards/voila', 0.5368069410324097, 'jupyter', 1), ('nalepae/pandarallel', 0.5356969237327576, 'pandas', 1), ('mwouts/jupytext', 0.5347379446029663, 'jupyter', 2), ('jupyter/nbformat', 0.5312812328338623, 'jupyter', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5288722515106201, 'pandas', 0), ('jupyter/notebook', 0.5267034769058228, 'jupyter', 1), ('jmcnamara/xlsxwriter', 0.5247606039047241, 'data', 1), ('zsailer/pandas_flavor', 0.5217926502227783, 'pandas', 1), ('parthjadhav/tkinter-designer', 0.5206194519996643, 'gui', 0), ('ipython/ipyparallel', 0.5092272162437439, 'perf', 0), ('zoomeranalytics/xlwings', 0.5089218616485596, 'data', 0), ('modin-project/modin', 0.5066853761672974, 'perf', 1), ('hoffstadt/dearpygui', 0.5062575936317444, 'gui', 0), ('rsheftel/pandas_market_calendars', 0.5060363411903381, 'finance', 1), ('ipython/ipykernel', 0.5052734017372131, 'util', 1), ('blaze/blaze', 0.5050551295280457, 'pandas', 0), ('dylanhogg/awesome-python', 0.5048226118087769, 'study', 1), ('mementum/bta-lib', 0.5043449401855469, 'finance', 0), ('plotly/plotly.py', 0.5021097660064697, 'viz', 1), ('masoniteframework/masonite', 0.5014203190803528, 'web', 0), ('rapidsai/cudf', 0.5010843276977539, 'pandas', 1)] | 4 | 2 | null | 0 | 0 | 0 | 56 | 16 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 20 |
392 | perf | https://github.com/klen/py-frameworks-bench | [] | null | [] | [] | null | null | null | klen/py-frameworks-bench | py-frameworks-bench | 699 | 86 | 27 | Python | https://klen.github.io/py-frameworks-bench/ | Another benchmark for some python frameworks | klen | 2024-01-03 | 2015-04-30 | 456 | 1.530497 | null | Another benchmark for some python frameworks | ['benchmark', 'python-frameworks'] | ['benchmark', 'python-frameworks'] | 2022-03-14 | [('ionelmc/pytest-benchmark', 0.6951150298118591, 'testing', 1), ('locustio/locust', 0.6128438115119934, 'testing', 0), ('fastai/fastcore', 0.6014686226844788, 'util', 0), ('neoteroi/blacksheep', 0.5975432395935059, 'web', 0), ('eleutherai/pyfra', 0.5963848829269409, 'ml', 0), ('lcompilers/lpython', 0.587522566318512, 'util', 0), ('wolever/parameterized', 0.5862182974815369, 'testing', 0), ('pypy/pypy', 0.5855749845504761, 'util', 0), ('pyutils/line_profiler', 0.5825835466384888, 'profiling', 0), ('pyston/pyston', 0.5815452933311462, 'util', 0), ('cython/cython', 0.5801135301589966, 'util', 0), ('pytoolz/toolz', 0.5774570107460022, 'util', 0), ('benfred/py-spy', 0.5661771297454834, 'profiling', 0), ('klen/muffin', 0.5657544732093811, 'web', 0), ('alirn76/panther', 0.564853310585022, 'web', 0), ('sumerc/yappi', 0.5642154216766357, 'profiling', 0), ('p403n1x87/austin', 0.5635471343994141, 'profiling', 0), ('mrdbourke/m1-machine-learning-test', 0.5594583749771118, 'ml', 0), ('pympler/pympler', 0.5578858256340027, 'perf', 0), ('carla-recourse/carla', 0.5568965077400208, 'ml', 1), ('pmorissette/bt', 0.5551998615264893, 'finance', 0), ('qdrant/vector-db-benchmark', 0.5516537427902222, 'perf', 1), ('rubik/radon', 0.5515268445014954, 'util', 0), ('google/gin-config', 0.5510473847389221, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5484030842781067, 'study', 0), ('exaloop/codon', 0.5465443730354309, 'perf', 0), ('grantjenks/python-diskcache', 0.5420731902122498, 'util', 0), ('joblib/joblib', 0.540972888469696, 'util', 0), ('faster-cpython/tools', 0.5376661419868469, 'perf', 0), ('dgilland/cacheout', 0.5362305641174316, 'perf', 0), ('geeogi/async-python-lambda-template', 0.5316784977912903, 'template', 0), ('nvidia/warp', 0.5304505825042725, 'sim', 0), ('bottlepy/bottle', 0.5283790826797485, 'web', 0), ('intel/intel-extension-for-pytorch', 0.5263614654541016, 'perf', 0), ('reloadware/reloadium', 0.5263360142707825, 'profiling', 0), ('python-trio/trio', 0.5250015258789062, 'perf', 0), ('libtcod/python-tcod', 0.5221824049949646, 'gamedev', 0), ('pyinfra-dev/pyinfra', 0.5210394263267517, 'util', 0), ('sfu-db/connector-x', 0.5208754539489746, 'data', 0), ('hyperopt/hyperopt', 0.5207222700119019, 'ml', 0), ('beeware/toga', 0.5207023024559021, 'gui', 0), ('jmcarpenter2/swifter', 0.519839346408844, 'pandas', 0), ('python-cachier/cachier', 0.5169395208358765, 'perf', 0), ('mementum/backtrader', 0.5150114893913269, 'finance', 0), ('nedbat/coveragepy', 0.514492928981781, 'testing', 0), ('hoffstadt/dearpygui', 0.511713981628418, 'gui', 0), ('astral-sh/ruff', 0.5096173882484436, 'util', 0), ('python-restx/flask-restx', 0.5092925429344177, 'web', 0), ('timofurrer/awesome-asyncio', 0.5063012838363647, 'study', 0), ('magicstack/uvloop', 0.5054547190666199, 'util', 0), ('ipython/ipyparallel', 0.5014100074768066, 'perf', 0), ('plasma-umass/scalene', 0.500389039516449, 'profiling', 0)] | 10 | 4 | null | 0 | 0 | 0 | 106 | 22 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 20 |
211 | time-series | https://github.com/firmai/atspy | [] | null | [] | [] | null | null | null | firmai/atspy | atspy | 499 | 89 | 21 | Python | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580631 | AtsPy: Automated Time Series Models in Python (by @firmai) | firmai | 2024-01-05 | 2020-01-28 | 209 | 2.38756 | null | AtsPy: Automated Time Series Models in Python (by @firmai) | ['automated', 'finance', 'forecasting', 'forecasting-models', 'time-series', 'time-series-analysis'] | ['automated', 'finance', 'forecasting', 'forecasting-models', 'time-series', 'time-series-analysis'] | 2021-12-18 | [('alkaline-ml/pmdarima', 0.7777550220489502, 'time-series', 3), ('awslabs/gluonts', 0.695907711982727, 'time-series', 2), ('winedarksea/autots', 0.6719325184822083, 'time-series', 2), ('statsmodels/statsmodels', 0.6599208116531372, 'ml', 1), ('unit8co/darts', 0.6426480412483215, 'time-series', 2), ('bashtage/arch', 0.6273349523544312, 'time-series', 3), ('scikit-learn/scikit-learn', 0.6164392232894897, 'ml', 0), ('rjt1990/pyflux', 0.6143859624862671, 'time-series', 1), ('tdameritrade/stumpy', 0.6093729138374329, 'time-series', 1), ('goldmansachs/gs-quant', 0.6063892841339111, 'finance', 0), ('google/temporian', 0.6023882031440735, 'time-series', 1), ('sktime/sktime', 0.5919238924980164, 'time-series', 3), ('uber/orbit', 0.5873233675956726, 'time-series', 2), ('ta-lib/ta-lib-python', 0.5853879451751709, 'finance', 1), ('stan-dev/pystan', 0.5848978757858276, 'ml', 0), ('ranaroussi/quantstats', 0.5767538547515869, 'finance', 1), ('nixtla/statsforecast', 0.5731773972511292, 'time-series', 2), ('cuemacro/finmarketpy', 0.5688939690589905, 'finance', 0), ('crflynn/stochastic', 0.5687724351882935, 'sim', 0), ('featurelabs/featuretools', 0.5671241283416748, 'ml', 0), ('pastas/pastas', 0.5670745372772217, 'time-series', 0), ('eleutherai/pyfra', 0.5660873055458069, 'ml', 0), ('gradio-app/gradio', 0.5611603856086731, 'viz', 0), ('ourownstory/neural_prophet', 0.5586233735084534, 'ml', 2), ('gbeced/pyalgotrade', 0.5548366904258728, 'finance', 0), ('salesforce/merlion', 0.5541254281997681, 'time-series', 2), ('automl/auto-sklearn', 0.5523808598518372, 'ml', 0), ('rasbt/mlxtend', 0.5510342121124268, 'ml', 0), ('google/pyglove', 0.5508860349655151, 'util', 0), ('pmorissette/ffn', 0.5481270551681519, 'finance', 0), ('scikit-mobility/scikit-mobility', 0.5477433800697327, 'gis', 0), ('pycaret/pycaret', 0.5466391444206238, 'ml', 1), ('microsoft/flaml', 0.5425217747688293, 'ml', 0), ('online-ml/river', 0.5391773581504822, 'ml', 0), ('nccr-itmo/fedot', 0.5334661602973938, 'ml-ops', 0), ('pytoolz/toolz', 0.5319315791130066, 'util', 0), ('awslabs/autogluon', 0.53136146068573, 'ml', 2), ('robcarver17/pysystemtrade', 0.5296515226364136, 'finance', 0), ('mljar/mljar-supervised', 0.5243828296661377, 'ml', 0), ('plotly/dash', 0.5219200849533081, 'viz', 1), ('pypy/pypy', 0.5205024480819702, 'util', 0), ('wilsonrljr/sysidentpy', 0.5202825665473938, 'time-series', 1), ('pandas-dev/pandas', 0.5182963013648987, 'pandas', 0), ('dateutil/dateutil', 0.5173526406288147, 'util', 0), ('linkedin/greykite', 0.5171492099761963, 'ml', 0), ('salesforce/deeptime', 0.5146031975746155, 'time-series', 2), ('microprediction/microprediction', 0.5109694004058838, 'time-series', 1), ('shankarpandala/lazypredict', 0.5108960866928101, 'ml', 0), ('facebook/prophet', 0.5104213356971741, 'time-series', 2), ('skops-dev/skops', 0.5083118081092834, 'ml-ops', 0), ('artemyk/dynpy', 0.5067782998085022, 'sim', 0), ('agronholm/apscheduler', 0.5056720972061157, 'util', 0), ('quantopian/pyfolio', 0.5053930282592773, 'finance', 0), ('kernc/backtesting.py', 0.5046423673629761, 'finance', 1), ('hydrosquall/tiingo-python', 0.5044682025909424, 'finance', 1), ('polyaxon/datatile', 0.5035507678985596, 'pandas', 0), ('selfexplainml/piml-toolbox', 0.5030118823051453, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.5014930367469788, 'ml', 0), ('epistasislab/tpot', 0.5008826851844788, 'ml', 0), ('gbeced/basana', 0.5008696913719177, 'finance', 0)] | 5 | 2 | null | 0 | 1 | 0 | 48 | 25 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 20 |
1,757 | diffusion | https://github.com/laion-ai/dalle2-laion | ['text-to-image', 'diffusion'] | null | [] | [] | null | null | null | laion-ai/dalle2-laion | dalle2-laion | 489 | 65 | 23 | Python | null | Pretrained Dalle2 from laion | laion-ai | 2024-01-12 | 2022-06-26 | 83 | 5.871355 | https://avatars.githubusercontent.com/u/92627801?v=4 | Pretrained Dalle2 from laion | [] | ['diffusion', 'text-to-image'] | 2022-11-09 | [('saharmor/dalle-playground', 0.570446252822876, 'diffusion', 1), ('borisdayma/dalle-mini', 0.5235878229141235, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5131522417068481, 'diffusion', 1), ('huggingface/diffusers', 0.5089276432991028, 'diffusion', 1)] | 5 | 1 | null | 0 | 0 | 0 | 19 | 14 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 20 |
1,804 | util | https://github.com/taylorsmarks/playsound | ['mp3', 'sound'] | null | [] | [] | null | null | null | taylorsmarks/playsound | playsound | 475 | 114 | 13 | Python | null | Pure Python, cross platform, single function module with no dependencies for playing sounds. | taylorsmarks | 2024-01-09 | 2016-01-27 | 417 | 1.136752 | null | Pure Python, cross platform, single function module with no dependencies for playing sounds. | [] | ['mp3', 'sound'] | 2021-08-06 | [('bastibe/python-soundfile', 0.6413252949714661, 'util', 0), ('spotify/pedalboard', 0.6269357204437256, 'util', 0), ('irmen/pyminiaudio', 0.6150918006896973, 'util', 0), ('quodlibet/mutagen', 0.6006749868392944, 'util', 1), ('uberi/speech_recognition', 0.5477955341339111, 'ml', 0), ('pytoolz/toolz', 0.5312017798423767, 'util', 0), ('asweigart/pyperclip', 0.5268693566322327, 'util', 0)] | 8 | 1 | null | 0 | 5 | 0 | 97 | 30 | 0 | 0 | 0 | 5 | 15 | 90 | 3 | 20 |
569 | gis | https://github.com/developmentseed/label-maker | [] | null | [] | [] | null | null | null | developmentseed/label-maker | label-maker | 453 | 114 | 52 | Python | http://devseed.com/label-maker/ | Data Preparation for Satellite Machine Learning | developmentseed | 2024-01-04 | 2018-01-10 | 315 | 1.434193 | https://avatars.githubusercontent.com/u/92384?v=4 | Data Preparation for Satellite Machine Learning | ['computer-vision', 'data-preparation', 'deep-learning', 'keras', 'remote-sensing', 'satellite-imagery'] | ['computer-vision', 'data-preparation', 'deep-learning', 'keras', 'remote-sensing', 'satellite-imagery'] | 2020-11-19 | [('azavea/raster-vision', 0.6791407465934753, 'gis', 3), ('microsoft/torchgeo', 0.629837691783905, 'gis', 4), ('plant99/felicette', 0.6269397139549255, 'gis', 1), ('datasystemslab/geotorch', 0.5983750224113464, 'gis', 1), ('remotesensinglab/raster4ml', 0.5611507296562195, 'gis', 1), ('fatiando/verde', 0.5453664660453796, 'gis', 0), ('aleju/imgaug', 0.5313600897789001, 'ml', 1), ('sentinelsat/sentinelsat', 0.5269395112991333, 'gis', 2), ('huggingface/datasets', 0.52162766456604, 'nlp', 2), ('awslabs/autogluon', 0.5088804364204407, 'ml', 2), ('googlecloudplatform/practical-ml-vision-book', 0.505915641784668, 'study', 0), ('sentinel-hub/eo-learn', 0.5026171803474426, 'gis', 0)] | 15 | 6 | null | 0 | 0 | 0 | 73 | 38 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 20 |
524 | nlp | https://github.com/hazyresearch/fonduer | [] | null | [] | [] | null | null | null | hazyresearch/fonduer | fonduer | 397 | 77 | 28 | Python | https://fonduer.readthedocs.io/ | A knowledge base construction engine for richly formatted data | hazyresearch | 2024-01-04 | 2018-02-02 | 312 | 1.27011 | https://avatars.githubusercontent.com/u/2165246?v=4 | A knowledge base construction engine for richly formatted data | ['knowledge-base-construction', 'machine-learning', 'multimodality'] | ['knowledge-base-construction', 'machine-learning', 'multimodality'] | 2021-06-23 | [] | 15 | 5 | null | 0 | 0 | 0 | 72 | 31 | 0 | 5 | 5 | 0 | 0 | 90 | 0 | 20 |
1,482 | util | https://github.com/proofit404/dependencies | ['dependency-injection'] | null | [] | [] | null | null | null | proofit404/dependencies | dependencies | 351 | 17 | 8 | Python | https://proofit404.github.io/dependencies/ | Constructor injection designed with OOP in mind. | proofit404 | 2024-01-06 | 2016-01-21 | 418 | 0.83828 | null | Constructor injection designed with OOP in mind. | [] | ['dependency-injection'] | 2022-11-01 | [('python-injector/injector', 0.554645836353302, 'util', 1), ('ivankorobkov/python-inject', 0.547492265701294, 'util', 1)] | 11 | 4 | null | 0 | 0 | 0 | 97 | 15 | 0 | 7 | 7 | 0 | 0 | 90 | 0 | 20 |
248 | sim | https://github.com/bilhim/trafficsimulator | [] | null | [] | [] | null | null | null | bilhim/trafficsimulator | trafficSimulator | 303 | 118 | 17 | Python | null | A microscopic traffic simulation in Python | bilhim | 2024-01-12 | 2021-09-05 | 125 | 2.418472 | null | A microscopic traffic simulation in Python | [] | [] | 2023-06-26 | [('crowddynamics/crowddynamics', 0.5411049723625183, 'sim', 0)] | 3 | 1 | null | 0.27 | 1 | 0 | 29 | 7 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 20 |
1,233 | llm | https://github.com/conceptofmind/toolformer | ['toolformer', 'language-model'] | Open-source implementation of Toolformer: Language Models Can Teach Themselves to Use Tools | [] | [] | null | null | null | conceptofmind/toolformer | toolformer | 296 | 33 | 12 | Python | null | null | conceptofmind | 2024-01-12 | 2023-02-17 | 49 | 5.971182 | null | Open-source implementation of Toolformer: Language Models Can Teach Themselves to Use Tools | [] | ['language-model', 'toolformer'] | 2023-03-04 | [('lucidrains/toolformer-pytorch', 0.7667592167854309, 'llm', 2), ('ctlllll/llm-toolmaker', 0.7394540309906006, 'llm', 1), ('openbmb/toolbench', 0.6644108891487122, 'llm', 0), ('lm-sys/fastchat', 0.5984368920326233, 'llm', 1), ('young-geng/easylm', 0.5841966867446899, 'llm', 1), ('salesforce/codet5', 0.5832077264785767, 'nlp', 1), ('guidance-ai/guidance', 0.5727112293243408, 'llm', 1), ('night-chen/toolqa', 0.5704953670501709, 'llm', 0), ('neulab/prompt2model', 0.5687054395675659, 'llm', 1), ('hannibal046/awesome-llm', 0.5643236041069031, 'study', 1), ('openlmlab/moss', 0.5642003417015076, 'llm', 1), ('oobabooga/text-generation-webui', 0.5621170997619629, 'llm', 1), ('hegelai/prompttools', 0.5603080987930298, 'llm', 0), ('thudm/codegeex', 0.5580187439918518, 'llm', 0), ('ai21labs/lm-evaluation', 0.5530425310134888, 'llm', 1), ('lianjiatech/belle', 0.5519489049911499, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5475521087646484, 'llm', 0), ('aiwaves-cn/agents', 0.5445042252540588, 'nlp', 1), ('argilla-io/argilla', 0.5423489809036255, 'nlp', 0), ('agenta-ai/agenta', 0.5377708673477173, 'llm', 0), ('bigscience-workshop/promptsource', 0.5377352237701416, 'nlp', 0), ('tigerlab-ai/tiger', 0.5376387238502502, 'llm', 0), ('juncongmoo/pyllama', 0.5372142195701599, 'llm', 0), ('prefecthq/langchain-prefect', 0.536091685295105, 'llm', 0), ('salesforce/codegen', 0.5336177945137024, 'nlp', 0), ('cg123/mergekit', 0.5336092710494995, 'llm', 0), ('kubeflow/examples', 0.5328210592269897, 'ml-ops', 0), ('nat/openplayground', 0.5319477915763855, 'llm', 1), ('infinitylogesh/mutate', 0.5267210602760315, 'nlp', 1), ('keirp/automatic_prompt_engineer', 0.5263444185256958, 'llm', 1), ('reasoning-machines/pal', 0.5196791887283325, 'llm', 1), ('freedomintelligence/llmzoo', 0.5184057950973511, 'llm', 1), ('hwchase17/langchain', 0.5181885361671448, 'llm', 1), ('salesforce/xgen', 0.5180943608283997, 'llm', 1), ('lupantech/chameleon-llm', 0.5162728428840637, 'llm', 1), ('nomic-ai/gpt4all', 0.5125241875648499, 'llm', 1), ('jalammar/ecco', 0.5121808648109436, 'ml-interpretability', 0), ('tatsu-lab/stanford_alpaca', 0.5121525526046753, 'llm', 1), ('next-gpt/next-gpt', 0.5089335441589355, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5054224729537964, 'llm', 1), ('eleutherai/the-pile', 0.5042285919189453, 'data', 0), ('selfexplainml/piml-toolbox', 0.5030021071434021, 'ml-interpretability', 0), ('hiyouga/llama-factory', 0.5014780759811401, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5014779567718506, 'llm', 1), ('ravenscroftj/turbopilot', 0.5007780194282532, 'llm', 1)] | 3 | 1 | null | 0.56 | 0 | 0 | 11 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 20 |
939 | nlp | https://github.com/ibm/transition-amr-parser | [] | null | [] | [] | null | null | null | ibm/transition-amr-parser | transition-amr-parser | 218 | 47 | 13 | Python | null | SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch. | ibm | 2024-01-10 | 2019-10-08 | 225 | 0.968889 | https://avatars.githubusercontent.com/u/1459110?v=4 | SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch. | ['abstract-meaning-representation', 'amr', 'amr-graphs', 'amr-parser', 'amr-parsing', 'machine-learning', 'nlp', 'semantic-parsing'] | ['abstract-meaning-representation', 'amr', 'amr-graphs', 'amr-parser', 'amr-parsing', 'machine-learning', 'nlp', 'semantic-parsing'] | 2023-05-09 | [('allenai/allennlp', 0.5920513272285461, 'nlp', 1), ('instagram/libcst', 0.5405070781707764, 'util', 0), ('pytorch/captum', 0.5239638090133667, 'ml-interpretability', 0), ('flairnlp/flair', 0.5182547569274902, 'nlp', 2), ('salesforce/blip', 0.5134626626968384, 'diffusion', 0), ('explosion/spacy', 0.5131350159645081, 'nlp', 2), ('explosion/spacy-llm', 0.5116180777549744, 'llm', 2)] | 17 | 2 | null | 1.92 | 1 | 0 | 52 | 8 | 0 | 4 | 4 | 1 | 0 | 90 | 0 | 20 |
936 | web | https://github.com/rawheel/fastapi-boilerplate | [] | null | [] | [] | null | null | null | rawheel/fastapi-boilerplate | fastapi-boilerplate | 190 | 20 | 3 | Python | null | Dockerized FastAPI boiler plate similar to Django code structure with views, serializers(pydantic) and model( Sqlalchemy ORM) with dockerized database(PostgresSQL) and PgAdmin. 🚀 | rawheel | 2023-12-27 | 2022-12-28 | 56 | 3.341709 | null | Dockerized FastAPI boiler plate similar to Django code structure with views, serializers(pydantic) and model( Sqlalchemy ORM) with dockerized database(PostgresSQL) and PgAdmin. 🚀 | ['alembic', 'boilerplate', 'docker', 'docker-compose', 'fastapi', 'fastapi-boilerplate', 'fastapi-sqlalchemy', 'orm', 'poetry-python', 'postgresql', 'pydantic', 'sqlalchemy', 'sqlalchemy-orm'] | ['alembic', 'boilerplate', 'docker', 'docker-compose', 'fastapi', 'fastapi-boilerplate', 'fastapi-sqlalchemy', 'orm', 'poetry-python', 'postgresql', 'pydantic', 'sqlalchemy', 'sqlalchemy-orm'] | 2023-08-29 | [('aeternalis-ingenium/fastapi-backend-template', 0.7447752356529236, 'web', 6), ('s3rius/fastapi-template', 0.6903481483459473, 'web', 4), ('asacristani/fastapi-rocket-boilerplate', 0.6740272045135498, 'template', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.6298112869262695, 'template', 3), ('fastai/fastcore', 0.6172206997871399, 'util', 0), ('vitalik/django-ninja', 0.6011227369308472, 'web', 1), ('fastapi-admin/fastapi-admin', 0.5968390107154846, 'web', 1), ('tiangolo/fastapi', 0.5945311188697815, 'web', 2), ('aminalaee/sqladmin', 0.5917928218841553, 'data', 2), ('collerek/ormar', 0.5596618056297302, 'data', 5), ('tiangolo/sqlmodel', 0.5594669580459595, 'data', 3), ('martinheinz/python-project-blueprint', 0.5532102584838867, 'template', 2), ('backtick-se/cowait', 0.5353572964668274, 'util', 1), ('starlite-api/starlite', 0.533804178237915, 'web', 1), ('buuntu/fastapi-react', 0.531681478023529, 'template', 4), ('multi-py/python-gunicorn', 0.5125967264175415, 'util', 1), ('python-restx/flask-restx', 0.5100096464157104, 'web', 0), ('multi-py/python-gunicorn-uvicorn', 0.5090985894203186, 'util', 1), ('ibis-project/ibis', 0.502396821975708, 'data', 2), ('bottlepy/bottle', 0.5016167759895325, 'web', 0), ('willmcgugan/textual', 0.5006387233734131, 'term', 0)] | 3 | 2 | null | 0.08 | 1 | 0 | 13 | 5 | 1 | 2 | 1 | 1 | 0 | 90 | 0 | 20 |
1,418 | llm | https://github.com/night-chen/toolqa | [] | null | [] | [] | null | null | null | night-chen/toolqa | ToolQA | 178 | 5 | 5 | Jupyter Notebook | https://arxiv.org/pdf/2306.13304.pdf | ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios. | night-chen | 2024-01-08 | 2023-06-06 | 34 | 5.235294 | null | ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios. | ['large-language-models', 'natural-language-understanding', 'natural-lauguage-processing', 'question-answering', 'tools'] | ['large-language-models', 'natural-language-understanding', 'natural-lauguage-processing', 'question-answering', 'tools'] | 2023-08-19 | [('rlancemartin/auto-evaluator', 0.641534149646759, 'llm', 1), ('deepset-ai/haystack', 0.6367813944816589, 'llm', 2), ('llmware-ai/llmware', 0.6234237551689148, 'llm', 2), ('young-geng/easylm', 0.6132665276527405, 'llm', 1), ('openbmb/toolbench', 0.6098852753639221, 'llm', 0), ('argilla-io/argilla', 0.6075721979141235, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.6017274260520935, 'study', 1), ('hegelai/prompttools', 0.5785123109817505, 'llm', 1), ('nebuly-ai/nebullvm', 0.5734456181526184, 'perf', 1), ('ibm/dromedary', 0.5732406377792358, 'llm', 0), ('conceptofmind/toolformer', 0.5704953670501709, 'llm', 0), ('salesforce/xgen', 0.5676834583282471, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5650957822799683, 'llm', 1), ('explosion/spacy-llm', 0.5622978806495667, 'llm', 1), ('lm-sys/fastchat', 0.5598968267440796, 'llm', 0), ('nomic-ai/gpt4all', 0.5544880032539368, 'llm', 0), ('eleutherai/the-pile', 0.5525684952735901, 'data', 0), ('openlmlab/moss', 0.549114465713501, 'llm', 1), ('whitead/paper-qa', 0.54909348487854, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5460361242294312, 'llm', 0), ('tigerlab-ai/tiger', 0.5423557758331299, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.542137086391449, 'llm', 0), ('agenta-ai/agenta', 0.539746880531311, 'llm', 1), ('defog-ai/sqlcoder', 0.5368439555168152, 'llm', 0), ('confident-ai/deepeval', 0.529060959815979, 'testing', 0), ('salesforce/codet5', 0.525848388671875, 'nlp', 1), ('iryna-kondr/scikit-llm', 0.522339940071106, 'llm', 0), ('bobazooba/xllm', 0.5220770835876465, 'llm', 1), ('pathwaycom/llm-app', 0.5218302607536316, 'llm', 0), ('srush/minichain', 0.5163832306861877, 'llm', 1), ('thudm/chatglm2-6b', 0.5134639143943787, 'llm', 1), ('microsoft/jarvis', 0.5131213068962097, 'llm', 0), ('dylanhogg/llmgraph', 0.5115826725959778, 'ml', 0), ('vllm-project/vllm', 0.5108919143676758, 'llm', 0), ('citadel-ai/langcheck', 0.5104190111160278, 'llm', 0), ('paddlepaddle/rocketqa', 0.5089247226715088, 'nlp', 1), ('deepset-ai/farm', 0.508597195148468, 'nlp', 1), ('aiwaves-cn/agents', 0.5083682537078857, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5064146518707275, 'llm', 0), ('ofa-sys/ofa', 0.5051456093788147, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5044512152671814, 'llm', 0), ('bigscience-workshop/petals', 0.5019873976707458, 'data', 1), ('cg123/mergekit', 0.5008574724197388, 'llm', 0), ('databrickslabs/dolly', 0.5000602006912231, 'llm', 0)] | 2 | 1 | null | 0.44 | 1 | 0 | 7 | 5 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 20 |
529 | gis | https://github.com/gdaosu/lod2buildingmodel | [] | null | [] | [] | null | null | null | gdaosu/lod2buildingmodel | LOD2BuildingModel | 139 | 28 | 11 | Python | null | SAT2LoD2: Automated LoD-2 Model Reconstruction from Satellite-derived DSM and Orthophoto | gdaosu | 2024-01-04 | 2021-08-30 | 126 | 1.101925 | https://avatars.githubusercontent.com/u/84828009?v=4 | SAT2LoD2: Automated LoD-2 Model Reconstruction from Satellite-derived DSM and Orthophoto | [] | [] | 2023-10-10 | [] | 2 | 2 | null | 0.02 | 1 | 0 | 29 | 3 | 0 | 0 | 0 | 1 | 2 | 90 | 2 | 20 |
1,905 | util | https://github.com/pomponchik/instld | [] | null | [] | [] | null | null | null | pomponchik/instld | instld | 44 | 0 | 2 | Python | null | The simplest package management | pomponchik | 2024-01-18 | 2023-04-02 | 43 | 1.016502 | null | The simplest package management | ['context-manager', 'package-manager', 'pip', 'venv'] | ['context-manager', 'package-manager', 'pip', 'venv'] | 2024-01-17 | [('mitsuhiko/rye', 0.748336672782898, 'util', 1), ('indygreg/pyoxidizer', 0.7080777883529663, 'util', 1), ('pypa/hatch', 0.6995571255683899, 'util', 1), ('pdm-project/pdm', 0.6940727829933167, 'util', 1), ('python-poetry/poetry', 0.6835312843322754, 'util', 1), ('mamba-org/mamba', 0.6645695567131042, 'util', 1), ('conda/conda', 0.6547331809997559, 'util', 1), ('pypa/pipenv', 0.6500003933906555, 'util', 2), ('pypa/flit', 0.6446828842163086, 'util', 1), ('spack/spack', 0.6380254030227661, 'util', 1), ('pyenv/pyenv', 0.6027212738990784, 'util', 2), ('pypi/warehouse', 0.5979329347610474, 'util', 0), ('thoth-station/micropipenv', 0.5940703749656677, 'util', 1), ('conda/conda-build', 0.5880274772644043, 'util', 0), ('omry/omegaconf', 0.5507912039756775, 'util', 0), ('jazzband/pip-tools', 0.5494071245193481, 'util', 1), ('ofek/pyapp', 0.5397077202796936, 'util', 0), ('google/gin-config', 0.5354675650596619, 'util', 0), ('pypa/setuptools_scm', 0.5312846302986145, 'util', 0), ('pypa/pipx', 0.5103548765182495, 'util', 2), ('pyodide/micropip', 0.5101442337036133, 'util', 0), ('mamba-org/boa', 0.5095345377922058, 'util', 0), ('citadel-ai/langcheck', 0.5082795023918152, 'llm', 0), ('bndr/pipreqs', 0.5049751996994019, 'util', 0), ('shishirpatil/gorilla', 0.5003833174705505, 'llm', 0)] | 2 | 0 | null | 6.29 | 6 | 6 | 10 | 0 | 2 | 4 | 2 | 6 | 4 | 90 | 0.7 | 20 |
1,411 | ml-interpretability | https://github.com/xplainable/xplainable | [] | null | [] | [] | null | null | null | xplainable/xplainable | xplainable | 39 | 4 | 3 | Python | https://www.xplainable.io | Real-time explainable machine learning for business optimisation | xplainable | 2024-01-08 | 2022-09-22 | 70 | 0.551515 | https://avatars.githubusercontent.com/u/98626943?v=4 | Real-time explainable machine learning for business optimisation | ['auto-ml', 'data-analytics', 'data-science', 'explainable-ai', 'explainable-ml', 'machine-learning', 'machine-learning-algorithms', 'prediction', 'predictions', 'shap', 'statistics', 'xai', 'xplainable'] | ['auto-ml', 'data-analytics', 'data-science', 'explainable-ai', 'explainable-ml', 'machine-learning', 'machine-learning-algorithms', 'prediction', 'predictions', 'shap', 'statistics', 'xai', 'xplainable'] | 2023-12-10 | [('interpretml/interpret', 0.6733729839324951, 'ml-interpretability', 4), ('winedarksea/autots', 0.6385115385055542, 'time-series', 1), ('oegedijk/explainerdashboard', 0.627983570098877, 'ml-interpretability', 2), ('seldonio/alibi', 0.6222488880157471, 'ml-interpretability', 2), ('csinva/imodels', 0.6195234656333923, 'ml', 5), ('microsoft/nni', 0.6121291518211365, 'ml', 3), ('mosaicml/composer', 0.5985530614852905, 'ml-dl', 1), ('nccr-itmo/fedot', 0.5892693996429443, 'ml-ops', 1), ('slundberg/shap', 0.5842406153678894, 'ml-interpretability', 2), ('online-ml/river', 0.584074079990387, 'ml', 2), ('bentoml/bentoml', 0.583275318145752, 'ml-ops', 1), ('feast-dev/feast', 0.5823587775230408, 'ml-ops', 2), ('mindsdb/mindsdb', 0.5784251093864441, 'data', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5782299637794495, 'study', 2), ('polyaxon/datatile', 0.5760681629180908, 'pandas', 3), ('google-research/google-research', 0.5756439566612244, 'ml', 1), ('automl/auto-sklearn', 0.5678261518478394, 'ml', 0), ('sktime/sktime', 0.5621213912963867, 'time-series', 2), ('firmai/industry-machine-learning', 0.5596107840538025, 'study', 2), ('mlflow/mlflow', 0.55943363904953, 'ml-ops', 1), ('maif/shapash', 0.551527738571167, 'ml', 3), ('awslabs/autogluon', 0.5499810576438904, 'ml', 2), ('netflix/metaflow', 0.5485888123512268, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5472549796104431, 'ml-ops', 2), ('huggingface/datasets', 0.5465106964111328, 'nlp', 1), ('ml-tooling/opyrator', 0.5456689596176147, 'viz', 1), ('onnx/onnx', 0.5391144156455994, 'ml', 1), ('googlecloudplatform/vertex-ai-samples', 0.53435218334198, 'ml', 1), ('pair-code/lit', 0.5334827303886414, 'ml-interpretability', 1), ('scikit-learn/scikit-learn', 0.5327098965644836, 'ml', 3), ('microsoft/flaml', 0.5324146747589111, 'ml', 2), ('polakowo/vectorbt', 0.532191812992096, 'finance', 2), ('eugeneyan/testing-ml', 0.5317504405975342, 'testing', 1), ('rafiqhasan/auto-tensorflow', 0.5286867618560791, 'ml-dl', 1), ('huggingface/autotrain-advanced', 0.5284545421600342, 'ml', 1), ('shankarpandala/lazypredict', 0.5252819061279297, 'ml', 1), ('hpcaitech/colossalai', 0.5240747928619385, 'llm', 0), ('salesforce/merlion', 0.5224214196205139, 'time-series', 1), ('tensorflow/tensor2tensor', 0.5187652707099915, 'ml', 1), ('microsoft/qlib', 0.5183536410331726, 'finance', 1), ('keras-team/autokeras', 0.5181999802589417, 'ml-dl', 1), ('teamhg-memex/eli5', 0.517871618270874, 'ml', 2), ('ourownstory/neural_prophet', 0.5147477984428406, 'ml', 2), ('ai4finance-foundation/finrl', 0.5115347504615784, 'finance', 0), ('explosion/thinc', 0.5094534754753113, 'ml-dl', 1), ('activeloopai/deeplake', 0.5084322094917297, 'ml-ops', 2), ('marcotcr/lime', 0.5037121176719666, 'ml-interpretability', 0), ('alpa-projects/alpa', 0.5035876035690308, 'ml-dl', 1), ('kubeflow/pipelines', 0.5017673969268799, 'ml-ops', 2), ('google/trax', 0.5001837015151978, 'ml-dl', 1)] | 3 | 1 | null | 5.62 | 32 | 25 | 16 | 1 | 5 | 6 | 5 | 32 | 2 | 90 | 0.1 | 20 |
333 | util | https://github.com/clarete/forbiddenfruit | [] | null | [] | [] | null | null | null | clarete/forbiddenfruit | forbiddenfruit | 794 | 52 | 29 | Python | https://clarete.li/forbiddenfruit/ | Patch built-in python objects | clarete | 2024-01-13 | 2013-04-03 | 564 | 1.405665 | null | Patch built-in python objects | ['monkey-patching'] | ['monkey-patching'] | 2022-03-12 | [('grahamdumpleton/wrapt', 0.6830537915229797, 'util', 0)] | 15 | 5 | null | 0 | 0 | 0 | 131 | 27 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |